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Working Group 2: Assessment and Measurement of the Environmental Efficiency of AI and Emerging Technologies

This Working Group develops assessment and measurement tools to evaluate the environmental performance, energy efficiency, and eco-friendless of emerging technologies. These tools include KPIs, metrics, scorecards, impact matrix and more. ​

No ​Provisional number


Environmental impact self-check assessment
​Technical Specification
​​This document will contain a scorecard for an organization to grade itself on how well they have built a product or service based upon environmental impacts. It will define a set of standard areas to be scored (e.g. power consumption, water consumption, etc.) as well as standardized scoring criteria so that scoring is measured the same across industries and products/services.​

Download the report here​
[Word | PDF]​​​​​
​Matthew Edgerton,

​Computer processing, data management and energy perspective
​Technical Report 
​We live in an era that is defined the “Cambrian explosion of data”, and advanced data analytics (Deep and Machine Learning, mainly) is ready to drive us in this world. The volume of data produced hourly and daily is enormous and is intended to dramatically increase in the next years –just consider the IoT revolution. Data centers of the future will be data driven. A clear limiting factor is their energy consumption. Presently, data centers consume more power than several European Union Member States​​, producing a larger footprint than all aircrafts. For these reasons, innovative strategies and technological solutions are needed to allow a scalability that is essential to enable and support the AI revolution. The document aims at recognizing important areas of innovation addressing this issue and facilitating the AI uptake by our Society.

Download the report here​
[Word | PDF]
Stefano Nativi,
European Commission ​
Requirements on energy efficiency measurement models and the role of AI and big data
​Technical Report
This document focuses on the impact of AI and big data on energy efficiency. It identifies a model that can calculate the energy efficiency in an urban space, from an AI and big data perspective.​ It uncovers the requirements for energy efficiency assessment, and features affecting energy demand. This document also defines a unified assessment model for energy efficient cities.​

Download the report here
[Word | PDF]​

​Leonidas Anthopoulos, University of Thessaly​​

Effective use cases on artificial intelligence for smart sustainable cities​ 

​Technical Report​This document will present effective use cases of technologies that contribute to sustainable smart cities.​


Download the report here​
[Word | PDF]​​​​​​
Abdelnasser Abdelaal, King Faisal University, Saudi Arabia
​Guidelines on Energy Efficient Blockchain Systems
​Technical Specification​
This document focuses on the impact of blockchain on energy efficiency. It provides an overview of blockchain's energy demand and consumption, defines blockchain's energy model and describes a set of energy efficiency parameters that can be calibrated in order to enhance blockchain's energy efficiency.

Download the report here
[Word | PDF]
Leonidas Anthopoulos, University of Thessaly​
Assessing Environmentally Efficient Data Centre and Cloud Computing in the framework of the Sustainable Development G​oals (SDGs)​
Technical Report​

This document aims to conduct an environmental sustainability assessment, encompassing the entire life cycle and factoring in a broad spectrum of energy and environmental problems that are needed to support the development of sustainably efficient data centres and cloud computing services​. ​The documents proposes  a multi-impact and life cycle approach and include the following aspects:​​


Download the report here
[Wo​rd | PDF]
​Paolo Bertoldi & Tiago Serrenho
European Commission, Joint Research Centre​