The AI Environmental Footprint: Between hidden costs and green opportunities
- Bias Rating
- Reliability
65% ReliableAverage
- Policy Leaning
-56% Medium Left
- Politician Portrayal
N/A
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Bias Score Analysis
The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
Sentiments
54% Positive
- Liberal
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|---|---|---|
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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Contributing sentiments towards policy:
68% : Porte noted that France has introduced the first national standard to measure AI's ecological footprint, contributes to EU-level standardization, and co-leads the Coalition for Sustainable AI with International Telecommunications Union (ITU) and the United Nations Environmental Programme (UNEP), which seeks to coordinate global efforts.57% : Victoria Frois, PhD Candidate, University of York and Research Associate of MSCA-DN: Understanding Latin American Challenges in the 21st Century (LAC-EU Network); Visiting Researcher, CIDOB This briefing compiles the key insights and conclusions from the session "The AI Environmental Footprint: Between Hidden Costs and Green Opportunities", organised by CIDOB in collaboration with the Barcelona City Council and the Cities Coalition for Digital Rights (CC4DR), and co-funded by the European Commission through the Citizens, Equality, Rights and Values (CERV) programme within the framework of the DigiDem-EU project.
54% : Data sovereignty and local control over digital infrastructure are essential to prevent disproportionate burdens on vulnerable regions, often in the Global South, which may host data centres or owing to weak environmental regulation, suffer the effects of mineral extraction.
51% : This raises a fundamental question: are these technologies genuinely advancing climate and social goals, or are they introducing new layers of inequality and environmental strain? With a view to exploring this discrepancy and supported by the Barcelona City Council, the Cities Coalition for Digital Rights (CC4DR), and the European Commission's CERV-funded DigiDem-EU project, CIDOB convened a panel as a side event of the Smart City Expo World Congress 2025 in Barcelona.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.
