Recommendation ITU-T L.1400: Overview and general principles of methodologies for assessing the environmental impact of information and communication technologies

Recommendation ITU-T L.1400 presents the general principles on assessing the environmental impact of information and communication technologies (ICTs) and outlines the different methodologies that have been developed in the L.1400-series: • Assessment of the environmental impact of ICT goods, networks and services • Assessment of the environmental impact of ICT projects • Assessment of the environmental impact of ICT in organizations • Assessment of the environmental impact of ICT in cities • Assessment of the environmental impact of the ICT sector • Assessment on how the use of ICT solutions impacts greenhouse gas (GHG) emissions of other sectors • Decarbonization trajectories for the ICT sector • Net zero guidance for ICT organizations • Guidance on how to address the ITU's Connect 20xx targets The Recommendation describes the intended usage of each Recommendation and the connections between them. Finally, it lists ongoing work items. 

https://www.itu.int/ITU-T/recommendations/rec.aspx?rec=15182

ITU-T L.1420 - Scope 3 guidance for telecommunication operators

 https://www.itu.int/ITU-T/recommendations/rec.aspx?rec=15671

Summary

Scope 3 emissions from telecommunication operators are the indirect emissions of their value chain, including their supply chain and products used by customers. Estimating Scope 3 emissions is difficult since this refers to emission sources outside a company's direct control.

Scope 3 emissions cover a wide range of economic activities that are divided into 15 Categories.

This Supplement establishes guidance to harmonize methods for telecommunication operators to assess and report their Scope 3 greenhouse gas (GHG) emissions, and to increase the coverage and transparency of the reporting. This guidance prioritizes Categories 1 to 2 and 11 of the GHG Protocol (which addresses the life cycle impact of company portfolios) in particular and Category 3 (which is closely linked to Scope 1 and 2), although all categories are addressed.

 

ITU-T Recommendations: Green ICT Standards and Supplements

 https://www.itu.int/net/ITU-T/lists/standards.aspx?Group=5&&&&Domain=28 

Towards the systematic reporting of the energy and carbon footprints of machine learning

 https://dl.acm.org/doi/abs/10.5555/3455716.3455964 

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime energy consumption and carbon emissions, as well as generating standardized online appendices. Utilizing this framework, we create a leaderboard for energy efficient reinforcement learning algorithms to incentivize responsible research in this area as an example for other areas of machine learning. Finally, based on case studies using our framework, we propose strategies for mitigation of carbon emissions and reduction of energy consumption. By making accounting easier, we hope to further the sustainable development of machine learning experiments and spur more research into energy efficient algorithms.

Sustainable AI: Environmental Implications, Challenges and Opportunities

 

Abstract

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

https://proceedings.mlsys.org/paper_files/paper/2022/file/462211f67c7d858f663355eff93b745e-Paper.pdf 

La transición verde y digital de Barcelona y sus impactos en el Sur Global

 

¿Qué implicaciones materiales tienen la apuesta por la movilidad eléctrica, la digitalización y el despliegue de las renovables sin un replanteamiento en términos de decrecimiento?

https://odg.cat/es/publicacion/mapa-la-transicion-verde-y-digital-de-barcelona-y-sus-impactos-en-el-sur-global/

Generative AI’s environmental costs are soaring — and mostly secret

 https://www.nature.com/articles/d41586-024-00478-x?mc_cid=700a41174d&mc_eid=7f01981b78