The recent IPCC report released in Apr 2022 reiterated the need to take collective action and more importantly to accelerate actions - “It’s now or never, if we want to limit global warming to 1.5°C (2.7°F),” said IPCC working group III co-chair Jim Skea. It will take all tools and innovations at our disposal to support the acceleration, including AI, which will offer a sizable and promising opportunity. Accordingly, AI is a ‘game changer’ for climate change and environmental issues.

- Predicting Climate change. The world’s climate scientists have the most difficult task: to predict with some accuracy that the future will be hotter than today. This requires several models that divide the planet’s atmosphere, ocean, forest, and land surface into a grid of cells, which is a complicated process. For example, calculating the state of the climate system for every minute of an entire century would require over 50m calculations for every grid cell. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models to show a range of predictions, which are then averaged out. AI is helping to determine which models are more reliable and thereby improving the accuracy of climate change projections.

- Circular Economy. Circular economy principles are meant to design out waste and pollution, keep products and materials in perpetual use, and regenerate natural systems. The advantages are substantial, for example, in Europe alone we could create a net benefit of Euro 1.8 trillion by 2030 . AI can play an important role in accelerating circular economy development by a) reducing time required to prototype b) supporting product as a service, at the operational stage, with better asset utilization, demand prediction, preventive maintenance, and smart inventory management, and c) optimizing circular infrastructure in closing the loop by improving the processes to sort and disassemble products, remanufacture components and recycle materials, components, and materials. A report by the Ellen MacArthur Foundation highlights that the potential value unlocked by applying AI in some or all the principles (Design, Operation, and Infrastructure) in the food industry is up to USD 127 billion a year in 2030. Equivalent AI opportunity in the consumer electronics is up to USD 90 billion a year in 2030

- AI for Energy. The electric grid is one of the most complex machines on Earth. However, it is evolving rapidly with the addition of variable renewable energy sources. Due to the inherent variability of wind and solar, the current grid faces many challenges in accommodating the diversity of renewable energy. The utility industry needs smart systems that can help improve the integration of renewables into the existing grid and make renewable energy an equal player in the energy supply. AI and IoT technologies can fill this gap by improving the reliability of renewable energy and modernizing the overall grid. Firstly, when coupled with AI, smart & centralized control centers offer flexibility to energy suppliers to cleverly adjust the supply with demand. Secondly, AI enables improved integration of microgrids. Thirdly, it improves safety and reliability with AI to manage intermittency. Fourthly, the integration of AI can help renewable energy suppliers expand the marketplace by introducing new service models and encouraging higher participation. A couple of big players like Xcel Energy and General Electric in the energy field are already harnessing the power of AI in the renewable energy space.

Artificial Intelligence has a huge potential for sustainability and climate change that is yet untapped. Organizations, while designing the architecture and capability for AI, should explore AI-enabled use cases from a climate action perspective. More importantly:

- Mitigating the carbon emissions generated from AI by adopting renewable energy and optimization techniques in their algorithms.

- Building a strong foundation for AI with the various use cases in mind including climate change.

- Collaborating and sharing, as this is critical in avoiding wasted efforts in duplicating models and helping to accelerate scale-ups. This should be done across industries, and across organization sizes including small and medium-sized organizations. This will minimize the overall cost and reduce the time required to bring AI into the market.

- Promoting innovation within and outside the organization. There has been an increased focus on ESG reporting as it is becoming mandatory. However, innovation is what will help organizations to really bring the emissions to the required level. We need to educate and promote AI technologies to employees and encourage experimentation, learning, failing to eventually succeed and accelerate the climate change reduction ambitions.