Meta, Google, Microsoft: AI Investments' ESG Impact

The meteoric rise of artificial intelligence (AI) is leaving an alarming imprint on public health and the environment.

Research from UC Riverside and Caltech reveals that pollution from data centres has cost the US more than US$5.4bn in healthcare costs over the last five years.

Data centres, the backbone of AI operations, are voracious consumers of electricity, often sourced from fossil fuels.
This reliance on non-renewable energy has led to significant air pollution, linked to a rise in respiratory diseases, cancer and other serious health conditions.

In 2023 alone, the health costs attributed to data centre pollution reached an estimated US$1.5bn, marking a 20% increase from the previous year.

Tech giants Google, Microsoft and Meta have been identified as the primary contributors to these public health expenses, with Google alone accounting for US$2.6bn over the five-year period.

Escalating energy demands

The AI boom shows no signs of slowing down.

By 2025, industry leaders Microsoft, Google, Amazon and Meta are projected to invest a combined US$320bn in AI infrastructure, more than doubling their 2023 expenditure of US$151bn. 

This surge in AI development is expected to have profound implications for energy consumption:

• The International Energy Agency warns that data centre electricity usage could double by 2026

• A single ChatGPT query consumes nearly ten times the electricity of a standard Google search

• Goldman Sachs predicts that AI-driven energy demand could push data centres to consume 10% of all US electricity by 2030, up from 4% in 2023

Beyond carbon: Water and waste concerns

The environmental impact of AI extends beyond carbon emissions.

Data centres require sophisticated cooling systems to function properly, leading to significant water consumption.

Google's 2024 Environment Report indicates an 88% increase in data centre water usage since 2019, exacerbating shortages in drought-prone regions like California.

Additionally, the disposal of data centre hardware presents a growing e-waste challenge.

AI chips and servers have limited lifespans and their components often contain toxic materials that pose serious health risks if not properly managed.

The consequences of AI's environmental footprint are not evenly distributed.

Data centres are frequently located in regions with cheaper land and lower regulatory barriers, such as West Virginia and Ohio.

These areas, often home to lower-income populations, bear the brunt of the resulting air and water pollution.

“Unlike carbon emissions, the health impacts caused by a data centre in one region cannot be offset by cleaner air elsewhere,” says Shaolei Ren, an Associate Professor at UC Riverside.

Sebastián Lehuedé, an Assistant Professor at King’s College London, adds: “There are so many issues around carbon credits from an ecological perspective.

“If you consume water somewhere to the point where it affects biodiversity in one area, that cannot be offset by having a nice project elsewhere. 

“You’re going to cause irreversible damage if you keep to that logic.”

Industry response and future outlook

Tech companies are making efforts to address these issues, primarily through renewable energy credits and innovative power solutions.

Microsoft, for instance, has partnered with Constellation Energy to restart a nuclear power unit in Pennsylvania, aiming to reduce fossil fuel dependence.

“To achieve our goal of becoming carbon negative by 2030, we will need a broad range of innovative carbon-free energy solutions,” says Melanie Nakagawa, Chief Sustainability Officer of Microsoft.

“Advanced nuclear energy and fusion energy are included in our multi-technology approach to reaching this target.”

However, experts warn that as long as AI development remains profit-driven, sustainability may continue to be a secondary concern.

Public protests against data centre expansion are becoming more frequent, with campaigners demanding increased oversight and accountability from the tech sector6.

“We are going to reach a tipping point where the increasing cost of data and hence, AI, is not just environmentally expensive but also socially expensive,” says Revathi Kollegala, a Digital Strategist at CIFOR-ICRAF.

“This will undermine the logic that AI can democratise access to knowledge and reduce inequity. 

“We may have reached that point already or will very soon.”

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