SCI — Software Carbon Intensity | Green Software Foundation

The Green Side of the Lua

The United Nations' 2030 Agenda for Sustainable Development highlights the importance of energy-efficient software to reduce the global carbon footprint. Programming languages and execution models...

Evaluating Sustainable Digitalization: A Carbon-Aware Framework for Enhancing Eco-Friendly Business Process Reengineering

In an era where sustainability is paramount, understanding the environmental impact of digitalizing business processes is critical. Despite the growing emphasis on sustainable practices, there is a lack of comprehensive methodologies to evaluate how digitalization impacts environmental sustainability compared to traditional processes. This paper introduces a carbon-aware methodological framework specifically designed to assess the sustainability of business process reengineering through digitalization. The Digital Green framework quantitatively analyzes the environmental costs associated with digital transformation, ensuring that truly sustainable digitalization results in lower resource consumption relative to the complexity of the process being digitalized. To demonstrate its effectiveness, the framework was applied to a case study involving the reengineering of an administrative process at a small university in southern Italy. The case study highlighted the framework’s ability to quantify the environmental benefits or detriments of digital transformation, thus guiding organizations toward more sustainable digital practices. This research contributes to the field by offering a concrete tool for aligning digitalization efforts with ecological sustainability, and by paving the way for integration with initiatives such as the Green Software Foundation’s Software Carbon Intensity (SCI) specifications.

Carbon

Carbon is the easiest way to create and share beautiful images of your source code.

Quantifying the Carbon Emissions of Machine Learning

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.

How to SNIFF out Good Scientific Software

New PLOS Computational Biology paper: "Ten Quick Tips to SNIFF Out Sustainable and Secure Scientific Software"

Sasha Luccioni, PhD 🦋🌎✨🤗 on Twitter / X

Man, this preprint is really the gift that keeps on giving.In case people missed my previous PSA : you can't compare the carbon emissions of people and objects. Humans are more than just the work that they do. (Also, that paper makes a lot of false assumptions in general) https://t.co/bZA414J9YI— Sasha Luccioni, PhD 🦋🌎✨🤗 (@SashaMTL) September 19, 2023

Linking the world's research to the code it runs on - OpenAlex blog

Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means […]

Radial — Science needs a new operating system.

We design, fund, and operate high-impact programs that prototype a new model for how science is done, shared, and scaled for real-world utility.

Improving Science That Uses Code

Abstract. As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being

Les émissions de CO₂ de Google et d’Amazon bondissent, propulsées par l’essor de l’IA

Les émissions totales de Google ont augmenté de 82 % depuis 2019 alors que le groupe s’était engagé à les réduire de moitié d’ici à 2030. Celles d’Amazon ont grimpé de 58 % au cours de la même période, malgré une neutralité carbone promise pour 2040.

wholegrain / Website Carbon Badges · GitLab

Badges to display your website's carbon emissions. - Work in progress!

carbon-design-system/carbon-components-svelte

Svelte implementation of the Carbon Design System

A Research Lab for Open Source ✸ Software Stewardship Lab

Today, we're launching a world-class research lab dedicated to Open Source sustainability with a team of some of the most experienced people in the world.

Managing scientific knowledge for policy on the ATmosphere - Mathew's newsletter

Two types of Atmosphere toolkit are needed if ATScience is to help scientists do science and better communicate it to other audiences: one serving the researchers and their teams, the other focused on aggregation and synthesis.

Ten quick tips to SNIFF out sustainable and secure scientific software

Modern computational biology depends heavily on open-source software tools, analysis pipelines, and containerized workflows developed and shared by the research community. While there is extensive guidance (including Quick Tips and Simple Rules articles) on how to build robust and sustainable scientific software, far less has been written for researchers in the role of software users evaluating whether an existing tool is reliable, secure, and sustainable enough for their work. Here we present ten quick tips to help researchers critically assess the tools they adopt. Our tips are organized around a framework that centers on key evaluation features: source, network, interaction, fit, and fragility (SNIFF). These dimensions prompt researchers to consider who maintains a tool and why, whether it is embedded in a broader ecosystem, how actively its developers and users engage, whether it matches the intended use case and licensing requirements, and how robust its dependencies and security practices are. By applying these tips, researchers can make more informed decisions, reduce the risk of relying on abandoned or insecure software, and contribute to a more sustainable scientific software ecosystem.