Is AI a Help or Hindrance to Reducing Emissions by 2030?
The London School of Economics and Political Science (LSE) and Systemiq have released findings on how AI can support climate goals.
The research focuses on AI's impact in power, transport and food sectors, which together account for nearly half of global emissions.
The study predicts that by 2035, AI can reduce emissions by 5.4 GtCO₂e annually, surpassing the energy AI itself consumes.
”From enabling smarter logistics to optimising energy grids, AI is already driving efficiency,” says Sophie Graham, Chief Sustainability Officer at IFS.
“But its impact goes further, equipping us with powerful advances in forecasting and early detection for severe weather events, critical to long-term resilience.
“IFS Industrial AI has a clear role to play in this transition, supporting asset-intensive industries adapting to a low-carbon future.
“Now is a pivotal moment to scale AI responsibly and equitably - especially where it can deliver the greatest climate value.”
Why AI matters in the climate transition
The report highlights AI's potential as a crucial enabler in transforming towards sustainability due to its widespread applicability and rapid innovation scalability.
The study's bottom-up approach analyses AI's capability to reduce emissions in power, food — specifically meat and dairy — and light road vehicles.
For example, AI-driven grid management and efficiency improvements could reduce power sector emissions by up to 1.8 GtCO₂e annually by 2035.
AI's support for adopting alternative proteins could save between 0.9 to 3.0 GtCO₂e annually in the food industry.
In transport, AI-enhanced shared mobility and increased electric vehicle use could cut emissions by up to 0.6 GtCO₂e annually.
Collectively, these efforts could lead to a combined reduction of up to 5.4 GtCO₂e each year.
AI in the power sector
According to the International Energy Agency (IEA), the power sector significantly contributes to global greenhouse gas emissions and integrating renewable energy sources is vital for achieving net-zero targets.
AI can forecast electricity supplies from renewable sources, ensuring demand is met while managing distributed energy resources like electric vehicles and batteries.
Notably, Google DeepMind's AI improved wind energy's ergonomic value by 20%, indicating significant potential for AI in optimising renewable energy operations.
By enhancing the load factor of wind and solar plants, more clean energy is generated from existing assets, lowering emissions per unit of energy produced.
AI's environmental footprint
Despite AI's potential for decarbonisation, it also has its environmental impact.
AI relies on data centres, which require substantial energy to operate and cool. As demand for AI grows, so does data centre electricity use.
However, the LSE and Systemiq study indicates that AI’s emission reduction capabilities far exceed its own carbon footprint.
The bodies estimate the emissions related to AI applications can range from 0.4 to 1.6 GtCO₂e, while potential reductions could be between 3.3 to 5.4 GtCO₂e annually.
While AI shows promise in reducing emissions, the research focuses on just three sectors and does not account for potential system-wide spillovers or rebound effects.
Additionally, it overlooks AI’s impacts on economic factors like investment and job creation.
“Policymakers must create enabling conditions for AI deployment, provide financial incentives for research and development, and ensure that AI applications are directed toward public goods and high-impact areas,” the paper says.
