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Frontier Technologies to Protect the Environment and Tackle Climate Change




               The limitations of AI

               While AI offers vast potential in the fight against climate change, as per the above examples, it must
               also be noted that there are certain limitations and downsides pertaining to its carbon footprint.
               These are explored in Box 9.


                           Box 9: The carbon footprint of AI and machine learning (ML)
                                                                              137, 138, 139, 140


                   The carbon cost of AI and ML

                   Artificial Intelligence (AI) and machine learning (ML), like most technology, have the
                   potential to help in the fight against climate change. They can make systems more efficient
                   (e.g. prevent electricity loss during transmission), enable remote sensing and automatic
                   monitoring (e.g. pinpoint deforestation, gather data on buildings, and track personal energy
                   use), provide fast approximations to time-intensive simulations (e.g. climate models and
                   energy scheduling models), and has the potential to lead to the development of interpretable
                   or causal models (e.g. for understanding weather patterns, informing policy makers, and
                   planning for disasters). Figure 12 shows some of the applications of ML.

                                      Figure 12: Applications of machine learning  [xii]
















                   It has been estimated, for instance, that ‘using AI for environmental applications could boost
                   the global economy by up to USD 5.2 trillion in 2030, a 4.4 per cent increase on the business-
                   as-usual scenario, while reducing GHG emissions worldwide by 4 per cent, equivalent to the
                   2030 annual emissions of Australia, Canada and Japan combined.’

                   The efficacy of ML and AI will, however, rely on bringing together several factors, including
                   ensuring their effective integration into other technologies and – because they require large
                   amounts of computing power – decarbonization of the energy system to ensure that AI and
                   ML can fulfil their sustainability potential. This is imperative as new studies are showing
                   that typical current ML processes can ‘emit more than 626 000 pounds of carbon dioxide
                   equivalent (CO e) – nearly five times the lifetime emissions of the average American car
                                2
                   (and this includes manufacture of the car itself)’.
                   The  significance  of  this  finding  is  immense,  and  until  the  issue  of  efficiency  can  be
                   addressed, AI research within academia may even be impacted due to lack of necessary
                   computational resources.















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