Artificial intelligence (AI) holds immense potential to drive decarbonisation, offering revolutionary benefits like optimised energy consumption, accelerated renewable energy deployment and enhanced transport systems.
However, this potential is not without its challenges, chiefly the substantial energy and water consumption of AI operations.
This paradox places sustainable investors in a difficult position, as they must balance the push for AI-driven transformation with the risk of increased carbon emissions if AI systems are not sustainably powered.
In the realm of investing, this issue is intensified.
Investors face the dual burden of decarbonising investment portfolios while ensuring profitability.
Explicit emissions targets often require rebalancing portfolios to stay within set carbon limits.
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Yet, as investments in AI technologies grow, so does the complexity of managing their environmental impact.
Environmental impact of AI
Training and operating advanced AI models consume enormous amounts of energy.
According to the International Energy Agency (IEA), a single Google search consumes 0.3 watt-hours (Wh) of electricity, while a ChatGPT request requires 2.9Wh, illustrating the large difference in computational expense.
While both figures in isolation appear small, there are profound environmental implications at scale.
Meanwhile, companies across industries are rapidly integrating AI for diverse applications ranging from cus- tomer service chatbots to AI-powered analytics tools that boost efficiency and decision-making processes.
For instance, in Singapore, an AI utility was launched in 2023 to combat greenwashing by using green building data to support decisions on sustainability-linked loans.
As the deployment of AI increases, so does global energy demand, leading to burgeoning electricity consumption.
This demand has even influenced policy decisions, such as the US delaying coal plant retirements to meet rising electricity needs.
AI’s role in mitigating its carbon footprint
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To effectively address the AI-decarbonisation paradox, an all-encompassing approach is crucial — aligning AI’s development and deployment with sustainable practices.
One logical strategy is to power AI infrastructure with renewable energy.
Notably, the global green data centre market is predicted to grow significantly, from US$81.12 billion ($105.05 billion) in 2024 to US$279.54 billion by 2032, largely driven by regulations encouraging renewable energy use in data centres.
Interestingly, AI itself could mitigate its environmental repercussions.
Although it may seem counterintuitive, advancements in AI and machine learning might offer solutions to their own energy demands.
Reports project that AI and machine learning will play a vital role in efficiently powering and cooling data centres worldwide.
For example, Google’s collaboration with DeepMind on algorithms and predictive modelling to reduce cooling power needs is a step toward sustainable solutions.
Enhancing AI’s energy efficiency, particularly in generative AI that requires substantial computational power, is critical for energy security and the scaling of AI applications. AI’s potential extends to climate intelligence by optimising energy grids, amplifying industrial efficiency and bolstering the deployment of renewable energy sources.
Synchronising wind turbines using AI, for example, can ensure optimal performance by aligning them with wind conditions — thus integrating technological advancements with environmental sustainability.
Ultimately, the development of nuclear fusion reactors will almost certainly rely heavily on AI to maintain the stability of the fusion plasma.
To support decarbonisation solutions, the European Union has introduced a series of incentives to champion AI-powered decarbonisation initiatives under the EU Green Deal and Energy Efficiency Directive.
Among these incentives are substantial financial grants from programmes like Horizon Europe, which has a budget of EUR95.5 billion ($137.32 billion), targeting AI-driven projects that contribute to the EU’s climate objectives.
Driving sustainable outcomes
From an investment perspective, capitalising on AI for decarbonisation presents a significant opportunity in the transition to a low-carbon economy, given the underlying AI systems are sustainably powered.
Shareholder proposals focused on responsible AI have gained traction, particularly at industry giants like Microsoft and Alphabet.
Following a shareholder proposal filed in 2023, Microsoft published its inaugural Responsible AI Transparency Report in 2024 and expanded the team responsible for ensuring its AI products are safe.
Investors can similarly engage with companies to drive substantive change, advocating for commitments to renewable energy, improvements in energy efficiency and transparent carbon footprint reporting.
Investing in AI companies that prioritise transparency and climate intelligence ensures these innovations are responsible and sustainable.
Impact investments are key to advancing AI-powered decarbonisation initiatives.
By directing funds into advanced technologies across diverse sectors such as agriculture, utilities and energy, these investments are driving AI-solutions that address critical issues such as biodiversity conservation, climate change modelling, transparency in AI model management and the circular economy.
While the inevitable rise in AI investments continues, a balanced approach is vital.
Hyperscalers will be challenged to adopt energy efficiency and renewable energy measures to keep its place in low carbon investment portfolios.
Simultaneously, we may see investors and policy makers take an active role in promoting responsible and sus- tainable AI applications soon.
We remain optimistic that a good balance between advancement in AI and sustainable energy usage can be achieved.
Mika Kastenholz is head of investment solutions, APAC, at LGT Private Bank. He is based in Hong Kong
