AI power use forecast finds the industry far off track to net zero – New Scientist

Report on the Environmental Impact of Artificial Intelligence and its Alignment with Sustainable Development Goals

The rapid expansion of the Artificial Intelligence (AI) industry presents a significant challenge to global sustainability commitments. A recent forecast indicates that the sector’s escalating resource consumption makes it unlikely to meet 2030 net-zero targets, creating direct conflicts with several United Nations Sustainable Development Goals (SDGs).

Projected Environmental Footprint and Contradiction with the 2030 Agenda

A modeling study conducted by researchers at Cornell University projects the energy, water, and carbon footprint of the US AI sector by 2030. The findings highlight a substantial environmental burden that directly undermines progress on key SDGs.

Key Projections and SDG Implications

• SDG 6: Clean Water and Sanitation: The industry is projected to require an additional 731 million to 1.125 billion cubic metres of water annually. This level of consumption places immense strain on local water resources, threatening water security and the sustainable management of this vital resource.
• SDG 13: Climate Action: Annual carbon dioxide emissions are forecast to be between 24 and 44 million tonnes. This significant increase in greenhouse gas emissions runs counter to the urgent action required to combat climate change and its impacts.
• SDG 7: Affordable and Clean Energy: The immense power usage required for AI servers challenges the transition to affordable, reliable, and sustainable energy systems, particularly if the electricity is sourced from non-renewable grids.

Strategic Mitigation Pathways for Sustainable AI Development

The report identifies several key strategies to mitigate the environmental impact of AI data centres. The effective implementation of these strategies is crucial for aligning the industry’s growth with the principles of sustainable development.

Recommended Actions for SDG Alignment

1. Strategic Siting of Infrastructure: Locating data centres in regions with ample water and high proportions of renewable energy, such as the Midwestern United States, is paramount. This approach supports SDG 6 by reducing water stress and SDG 7 by leveraging cleaner energy grids. It also contributes to SDG 11 (Sustainable Cities and Communities) by minimizing environmental strain on local populations.
2. Decarbonisation of Energy Supply: A fundamental shift towards renewable energy sources for powering data centres is essential. This directly addresses the objectives of SDG 7 and is the most critical factor in achieving the goals of SDG 13.
3. Enhancement of Operational Efficiency: Improving the efficiency of both computing and cooling processes can significantly reduce resource consumption. This aligns with SDG 9 (Industry, Innovation, and Infrastructure) by promoting resource-efficient technologies and SDG 12 (Responsible Consumption and Production) by fostering more sustainable industrial practices.

Collectively, these approaches could potentially reduce the industry’s carbon emissions by 73 per cent and its water footprint by 86 per cent.

Barriers to Sustainable Implementation

Despite potential solutions, significant challenges hinder the sustainable growth of AI infrastructure, including public opposition and a lack of corporate transparency.

Challenges and the Need for Accountability

• Community Opposition and SDG 11: In states like Virginia, Pennsylvania, and Arizona, local communities are opposing new data centre construction due to concerns over the extractive impact on water reserves and the local environment. This reflects a conflict with the goal of creating sustainable and resilient communities under SDG 11.
• Lack of Transparency and SDG 12: Experts in the field highlight a critical need for greater transparency. The call for AI developers to track and report their energy and resource consumption is a core tenet of corporate accountability, essential for achieving SDG 12. Such transparency would empower policymakers and users to make informed decisions and hold corporations accountable for their environmental commitments.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 6: Clean Water and Sanitation
   • The article extensively discusses the significant water consumption of AI data centers. It states that by 2030, US AI servers will require “between 731 million and 1.125 billion additional cubic metres of water.” It also highlights community concerns over the impact on “water reserves,” directly linking the industry’s growth to the sustainable management of water resources.
2. SDG 7: Affordable and Clean Energy
   • The article focuses on the high energy demand of the AI industry, mentioning “server power usage” and the need for “decarbonising energy supplies.” It suggests locating data centers in areas where the energy grid is “powered by a higher proportion of renewables” and notes the need to “invest in additional renewable energy capacity,” which are central themes of SDG 7.
3. SDG 9: Industry, Innovation, and Infrastructure
   • The article’s subject is the infrastructure of the AI industry—data centers. It addresses the need to make this infrastructure more sustainable by “improving the efficiency of data centre computing and cooling processes.” This aligns with SDG 9’s goal of building resilient and sustainable infrastructure.
4. SDG 11: Sustainable Cities and Communities
   • The article mentions “public opposition to data centre installations” in several states, including Virginia, Pennsylvania, and Texas. This opposition is based on the “extractive impact on the environment,” specifically on local “water reserves and the wider environment.” This connects to the goal of managing the environmental impact of industrial development on communities.
5. SDG 12: Responsible Consumption and Production
   • The core issue discussed is the unsustainable consumption of natural resources (energy and water) by the AI industry. The call for “more transparency” by “requiring model developers to track and report their compute and energy use” directly relates to promoting sustainable production and corporate accountability.
6. SDG 13: Climate Action
   • The article directly addresses the climate impact of AI, forecasting that the industry will be “emitting the equivalent of between 24 and 44 million tonnes of carbon dioxide a year.” It also discusses the industry’s challenge in meeting “net zero targets by 2030,” which is a key component of global climate action.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater.
   • The article’s forecast of up to “1.125 billion additional cubic metres of water” consumption and the mention of potential solutions that could cut the industry’s “water footprint by 86 per cent” directly relate to water-use efficiency and sustainable withdrawals.
2. Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix.
   • The article identifies placing data centers in regions with an energy grid “powered by a higher proportion of renewables” as a key strategy to reduce environmental impact, aligning with this target.
3. Target 7.3: By 2030, double the global rate of improvement in energy efficiency.
   • The recommendation to limit environmental impact by “improving the efficiency of data centre computing and cooling processes” directly supports the goal of enhancing energy efficiency.
4. Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
   • The entire discussion revolves around making AI infrastructure (data centers) more sustainable through efficiency improvements, decarbonized energy, and better location choices, which perfectly matches this target.
5. Target 12.6: Encourage companies, especially large and transnational companies, to adopt sustainable practices and to integrate sustainability information into their reporting cycle.
   • The article concludes by highlighting a need for “more transparency” and “requiring model developers to track and report their compute and energy use, and to provide this information to users and policymakers.” This is a direct call for the actions described in Target 12.6.
6. Target 13.2: Integrate climate change measures into national policies, strategies and planning.
   • The article’s focus on the AI industry’s carbon emissions and its struggle to meet “net zero targets” reflects the integration of climate change measures (like emissions reduction goals) into corporate and industrial strategy.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Volume of Water Consumption:
   • The article explicitly provides a quantitative forecast: “between 731 million and 1.125 billion additional cubic metres of water by 2030.” This figure can be used as a direct indicator to track the water footprint of the AI industry.
2. Volume of Carbon Dioxide Emissions:
   • The article quantifies the industry’s potential emissions as “between 24 and 44 million tonnes of carbon dioxide a year.” This serves as a key indicator for measuring the climate impact and progress towards net-zero targets.
3. Percentage Reduction in Environmental Footprint:
   • The article suggests that a combination of strategies “could cut the industry’s emissions by 73 per cent and its water footprint by 86 per cent.” These percentages can be used as benchmarks or indicators of progress in sustainability efforts.
4. Share of Renewable Energy in the Energy Mix:
   • The article implies this indicator by stating that locating data centers in areas with a “higher proportion of renewables” reduces their impact. Tracking the percentage of renewable energy used by data centers would measure progress.
5. Corporate Sustainability Reporting:
   • The call for companies “to track and report their compute and energy use” implies an indicator: the number or percentage of AI companies that publicly report their environmental impact data. This measures transparency and accountability.

4. Summary Table of SDGs, Targets, and Indicators

Source: newscientist.com