GreenDIGIT Deliverable D5.2 Energy Measurement and Impact Assessment Framework and Methodology and Developer Tooling

As Digital Research Infrastructures (DRIs) expand in scale and complexity, their environmental impact has become increasingly significant. While DRIs are essential to the advancement of modern science, they also consume large amounts of energy, generating substantial emissions, and depend on resource-intensive hardware and data services. In this context, there is growing pressure on research institutions and service providers to align their operations with European sustainability regulations and long-term climate goals.

The GreenDIGIT project’s Deliverable D5.2 addresses this challenge by focusing on the usage phase of DRIs, with particular attention to how the environmental impact of experimental research conducted within these infrastructures can be quantified and reduced. The deliverable proposes methods and tools targeted to users of DRIs, to support the assessment and mitigation of environmental impacts caused by their experiments.

Key challenges lie in performing lifecycle assessments of experiments. In accordance with Lifecycle Analysis (LCA) methodology and ISO 14000, these include accounting for both direct impacts (such as energy consumption during execution) and indirect impacts (such as the embodied impacts of the underlying infrastructure). While infrastructure operators may have access to relevant data, it is often not accessible to users or structured in a way that supports comprehensive LCA.

To address these gaps, this report presents methodological frameworks and practical tools for users of DRIs. It introduces a user-accessible database that details the environmental footprint of research infrastructure. In addition, it offers a Jupyter plugin for sustainability monitoring and prediction, to raise developers’ awareness for the impact of their experiments.

The report also proposes a metadata specification that enables the sharing of end-to-end energy consumption and environmental impact of complex experiments, such as IoT projects, as open data. To support practical adoption and ensure a high technology readiness level, the deliverable includes both technical specifications and prototype implementations of these tools.