Date posted: 17/11/2021

AI and the environmental impact on supply chains

CA ANZ report looks at AI’s carbon footprint

In brief:

  • CA ANZ’s new report on Ethics for Sustainable AI Adoption also looks at the environmental impact of AI through supply chains
  • The report is based on more than 5,700 interviews with accountants and finance professionals around the world
  • The report suggests that while AI creates a greater demand for energy, it can also be a part of the solution to cut emissions

Artificial intelligence is often perceived in abstract terms because ‘intelligence’ conjures up ideas of the intangible. 

In fact, the AI supply chain is very tangible, involving real materials and natural resources, and at the heart of this is the amount of energy it takes to run computer algorithms.

The absolute amount of data has surpassed all previous levels and is growing exponentially, and much of it is unstructured. This increasingly critical challenge is one of the issues addressed in a report – Ethics for Sustainable AI Adoption – commissioned by ACCA and Chartered Accountants ANZ.

The research, based on interviews with 5,723 accounting and finance professionals around the world and backed up with qualitative discussion groups, presents a snapshot of current AI adoption and the implications for the environment.  

The report highlights that AI is central to addressing the data explosion and the use of algorithms to tackle data that is increasingly voluminous, more varied, and unstructured, seems assured. 

This adoption has already begun and, depending on the use-case, between 7% and 19% of survey respondents currently use AI in their organisations.

As the use of AI intensifies across the economy, the collective usage of energy needed to power them will also increase. It takes time and energy, in the form of electricity, to run algorithms.

Looking across the supply chain, AI systems have an identifiable carbon footprint, and it’s not trivial. 

By one estimate, the computational power needed for training the largest AI models has been growing exponentially, doubling every 3-4 months.

Looking across the supply chain, AI systems have an identifiable carbon footprint, and it’s not trivial. 

The energy used by computing systems eventually dissipates as heat and so large data centres running AI algorithms can also require significant energy for cooling systems. 

Data centres consume energy by the nature of their activities and conventional production of that energy results in the emission of greenhouse gases.

In addition, toxic materials or rare earth metals may be needed for cooling or other support processes. For larger centres, a significant amount of energy is spent not on applications but just to make sure the centre is operable, such as for uninterrupted power and lighting.

One study showed that the carbon emissions for training a single natural language processing model was equivalent to 125 round-trip flights between New York and Beijing. More complex algorithms, such as GPT-3, which is used for language and text analysis, use even more. 

AI can, however, also be part of the solution. In the words of one survey participant: "The main impact AI has on creating a sustainable planet is that it enables efficient use of natural resources by closely monitoring the consumption pattern."  

Multidimensional real-time interactions between data centre equipment, cloud infrastructure, cooling systems, electricity generators and human operators can be modelled using machine learning. This is being used to infer use patterns, in one instance resulting in a 40% reduction in energy required for cooling.

AI can play a role in working towards the United Nation’s 17 Sustainable Development Goals (SDGs). 

SDG12, for example, is about the need to "ensure sustainable consumption and production patterns", which may be enabled by judicious use of AI. 

One study notes that of the 169 targets across the 17 SDGs, AI is anticipated to enable the accomplishment of 134, although it will also inhibit 59.

As AI enters the mainstream, finance professionals will sign off budgets to procure systems for their organisations. Typically, the business case will consider the predictive accuracy of the algorithm and monetised value of insight versus the costs and process implications of procuring it. 

In practice, this may mean considering trade-offs if there comes a point where further improvements in model accuracy require disproportionately greater energy consumption. 

Accountancy and finance professionals need the necessary awareness and skills to question the environmental implications of AI through their supply chains and have the ethical commitment to act.

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