AI Is Power-Hungry, but It Could Eventually Cut More Emissions Than It Creates – Scientific American

Artificial Intelligence and Climate Change: Potential for Emission Reductions Aligned with Sustainable Development Goals

Overview

Artificial intelligence (AI) has the potential to significantly reduce global climate pollution by up to 5.4 billion metric tons annually over the next decade. This reduction is achievable if AI is strategically applied to improve transportation, energy management, and food production systems. These findings are based on research conducted by the Grantham Research Institute and published in the journal npj Climate Action.

AI’s Dual Impact on Climate and Energy Consumption

• While AI-related data centers are energy-intensive and contribute to emissions, the overall emission reductions from AI applications could outweigh these increases.
• The study emphasizes the importance of directing AI towards accelerating market adoption of low-carbon solutions, highlighting the critical role of governments in regulation and support.

Governmental Role and Policy Recommendations

1. Regulate AI to minimize environmental footprints by promoting energy-efficient AI models.
2. Encourage the use of renewable energy sources to power data centers.
3. Invest in AI infrastructure and education, especially in developing countries, to ensure equitable distribution of AI benefits.

Challenges and Context

• AI’s rapid growth has raised concerns about its energy demands, with projections indicating data centers may consume twice as much electricity by 2030.
• Current trends show fossil fuels will continue to supply most energy for data centers, potentially undermining carbon reduction efforts.
• Energy demands from AI are already challenging power grids, particularly in the United States.

AI Applications Supporting Sustainable Development Goals (SDGs)

The study identifies five key areas where AI can contribute to emission reductions, directly supporting multiple SDGs:

1. Consumer Behavior: AI can influence sustainable consumption patterns, aligning with SDG 12: Responsible Consumption and Production.
2. Energy Management: AI enhances integration of renewable energy into power grids by forecasting supply and demand, supporting SDG 7: Affordable and Clean Energy and SDG 13: Climate Action.
3. Technology Innovation: AI-driven improvements in battery technology reduce electric vehicle costs, promoting SDG 9: Industry, Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities.
4. Food Production: AI can identify alternative proteins to replace high-emission meat and dairy products, contributing to SDG 2: Zero Hunger and SDG 15: Life on Land.
5. Transportation: Encouraging shared transport and electric vehicle adoption reduces emissions, supporting SDG 11: Sustainable Cities and Communities.

Projected Impact and Limitations

• The combined AI-driven actions could reduce emissions by 3.2 to 5.4 billion metric tons of CO₂ equivalent annually by 2035, surpassing the current emissions of the entire European Union.
• Although these reductions are significant, they are insufficient alone to limit global temperature rise to 1.5°C above pre-industrial levels.
• Estimated emissions from AI and data centers range from 0.4 to 1.6 billion metric tons of CO₂ equivalent over the next decade.
• The study acknowledges limitations due to the rapidly evolving AI landscape and potential rebound effects where efficiency gains may increase overall consumption.

Conclusion and Recommendations

The research underscores the importance of active government involvement to ensure AI’s environmental benefits are maximized while mitigating its negative impacts. Effective governance and strategic application of AI can significantly contribute to achieving the Sustainable Development Goals, particularly those related to climate action, clean energy, sustainable cities, and responsible consumption.

Source: Reprinted with permission from E&E News, POLITICO, LLC, 2025.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 7: Affordable and Clean Energy
   • The article discusses the use of AI to improve energy management, integrate renewables into the grid, and increase energy efficiency.
2. SDG 9: Industry, Innovation and Infrastructure
   • AI infrastructure development and technology innovation are highlighted as key to reducing emissions and improving energy systems.
3. SDG 12: Responsible Consumption and Production
   • AI applications influencing consumer behavior and promoting sustainable food production (e.g., protein alternatives) are mentioned.
4. SDG 13: Climate Action
   • The core focus is on AI’s potential to reduce global greenhouse gas emissions and mitigate climate change.
5. SDG 10: Reduced Inequalities
   • The article emphasizes equitable sharing of AI benefits, especially through investments in AI infrastructure and education in developing countries.

2. Specific Targets Under Those SDGs Identified

1. SDG 7: Affordable and Clean Energy
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
   • Target 7.3: Double the global rate of improvement in energy efficiency.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
3. SDG 12: Responsible Consumption and Production
   • Target 12.2: Achieve the sustainable management and efficient use of natural resources.
4. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies and planning.
5. SDG 10: Reduced Inequalities
   • Target 10.2: Empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

3. Indicators Mentioned or Implied to Measure Progress

1. Energy Consumption and Emissions from Data Centers
   • Indicator: Metric tons of CO2 equivalent emissions from data centers and AI-related energy use (implied by the article’s mention of emissions ranging from 0.4 to 1.6 billion metric tons).
2. Reduction in Global Greenhouse Gas Emissions
   • Indicator: Annual reduction in metric tons of CO2 equivalent emissions attributable to AI applications (e.g., 3.2 to 5.4 billion metric tons reduction by 2035).
3. Renewable Energy Integration
   • Indicator: Increased share of renewable energy in the electricity grid facilitated by AI forecasting and management.
4. Energy Efficiency Improvements
   • Indicator: Rate of improvement in energy efficiency in sectors using AI technology.
5. Equitable Access to AI Benefits
   • Indicator: Investments in AI infrastructure and education in developing countries (implied by the article’s emphasis on equitable sharing).

4. Table of SDGs, Targets and Indicators

Source: scientificamerican.com