# GreenDIGIT Tools for Environmental Sustainability in Digital Research Infrastructures: Assessment Methodology and Metrics Publication System

- Institution: CERN
- Taught by: Damla Rowlandson, Shashikant Ilager, Iida Lehto
- Year: 2026
- Language: English
- Subjects: Mathematics
- Source: [CERN](https://videos.cern.ch/record/3025539)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

The GreenDIGIT project has developed a comprehensive environmental sustainability framework for digital Research Infrastructures (RIs). In this interactive session, we introduce and demonstrate two key tools delivered by the project. First, the Environmental Impact Assessment Methodology offers a structured, lifecycle-based framework for evaluating carbon emissions, energy consumption, waste, and water use. It prioritises improvement actions across three dimensions: impact magnitude, likelihood, and implementation effort. This is complemented by a Self-Assessment Questionnaire that benchmarks RI practices against European policy and regulatory requirements, supporting ex ante planning, structured prioritisation, and ex post monitoring and reporting. Second, the Environmental Impact Metric Publication System (EIMPS) operationalises sustainability assessments at scale by enabling the automated submission, validation, normalisation, and dissemination of environmental metrics across heterogeneous Grid, Cloud, IoT, and Network infrastructures. Built on a Common Information Model (CIM) and a dedicated Metrics Database, EIMPS provides customisable reporting on energy intensity and operational efficiency. Together, these tools equip digital RIs with a coherent, adaptable solution for consistent sustainability evaluation and transparent environmental reporting

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## Lectures

1. [GreenDIGIT Tools for Environmental Sustainability in Digital Research Infrastructures: Assessment Methodology and Metrics Publication System](https://invisiblenetwork.ai/learn/cern/3025539/73f7b62418c2), 46 min (CERN's player)