Research from the London School of Economics and Political Science (LSE) and Systemiq highlights AI's role in climate initiatives.
By leveraging advancements in artificial intelligence (AI), the focus is on the power, transport and food sectors, covering nearly half of worldwide emissions.
The study predicts that by 2035, AI could potentially reduce emissions by 5.4 gigatonnes of CO2 equivalent (GtCO₂e) annually, surpassing its energy consumption.
”From enabling smarter logistics to optimising energy grids, AI is already driving efficiency,” said Sophie Graham, Chief Sustainability Officer at IFS, on LinkedIn.
“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
According to the researchers, AI can be a substantial catalyst in advancing sustainability because of its extensive applicability and rapid innovation scaling capabilities.
This extends beyond being a mere technological tool, allowing for systemic shifts in various industries.
It aims to escalate economic growth, disrupt under-investment cycles and significantly reduce emissions.
Employing a bottom-up methodology, the study evaluates AI’s influence in the power, food (meat and dairy) and mobility (light road vehicles) sectors for potential emissions reduction.
AI-enabled grid management and efficiency upgrades in the power sector could reduce emissions by up to 1.8 GtCO₂e by 2035. In the food industry, AI-driven adoption of alternative proteins might curtail emissions between 0.9 and 3 GtCO₂e annually.
For mobility, AI-supported shared transport and electric vehicle adoption may cut emissions by up to 0.6 GtCO₂e per year, culminating in an overall potential reduction of 5.4 GtCO₂e annually.
AI in the power sector
With a substantial portion of global greenhouse emissions originating from the power sector, integration of renewable sources like solar and wind becomes vital for achieving net-zero targets, as per the International Energy Agency (IEA).
AI assists in anticipating renewable electricity supplies to harmonise them with demand while also managing distributed energy assets such as electric vehicles and batteries.
Google DeepMind’s AI, for instance, increases wind energy's ergonomic value by 20% through efficient standby power needs.
Additionally, improved operation optimisation could bolster the load factor of wind and solar power plants, generating more clean energy from the same resources while minimising emissions per energy unit produced.
Environmental considerations in AI implementation
While AI presents considerable decarbonisation potential, it has intrinsic environmental impacts.
Data centres form the foundation of AI, where models are both trained and executed. The energy demands of these centres and their cooling processes can be significant.
As AI applications expand, the need for data centre capacity, along with its electric consumption, swells. Nonetheless, the LSE and Systemiq study asserts that prospective reductions outweigh increased emissions linked to heightened data centre power needs for AI.
These emissions are projected to range between 0.4 and 1.6 GtCO₂e, while potential reductions are estimated between 3.3 and 5.4 GtCO₂e.
Despite the promising findings, the research currently extends only to three sectors and does not encompass system-wide implications or rebound effects.
Additionally, it excludes AI's influence on investment or employment generation.
Recognising this, the paper concludes: "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."
