With many industries experiencing a digital transformation, Artificial Intelligence (AI) has emerged as a main driver in technology-driven projects. Although the development and training of AI contributes to the carbon footprint, AI has the potential to provide innovative solutions for building environmental sustainability, such as reducing carbon emissions, managing food waste, developing more eco-friendly transportation networks, and other issues to tackle global climate change. Also, AI is expected to enable more targets of the Sustainable Development Goals(SDG) categorized into the three main pillars than those that are inhibited by AI.
According to Forbes articles, AI’s capability of impact decoupling and resource decoupling are major benefits of using it as a technology for tackling challenges imposed by climate change. Not only can AI be used to decrease carbon dioxide emissions in the energy sector by forecasting the supply and demand of power, but it can also be used to create more efficient processes for managing renewable energy as well as reducing fossil fuel emissions. Moreover, management of resources from environments and raw materials can be optimized which allows to create more from less. For instance, with food systems, crop yields can be better monitored and help reduce any excess use of water and chemicals as well as reduce food waste by predicting demand and finding spoiled food. Moreover, with AI, energy use can be minimized as human errors are reduced. More efficient processes can be developed for businesses by removing unnecessary steps.
Environmental sustainability and tackling global climate change are part of the strategies of major companies in varying industries. Startups that employ AI for environmental sustainability are also emerging as environmental sustainability is becoming the core goal of many businesses. For instance, AI startup Greyparrot is an example of how AI is used for achieving net-zero waste. The startup developed an “AI-powered computer vision software to increase transparency and automation in recycling.” According to the CEO, a vision system is applied in an automated waste monitoring system where deep learning (AI) based computer vision models are used to identify the material of the waste from 40 and more waste categories. Then, the data is displayed in real-time for data analytics of the waste. For instance, in South Korea, an impurities detector has been applied to help with sorting and increase the purity of the recycled PET plastic which increases the chance of recycling.
Furthermore, in response to the negative environmental impacts of AI, Google DeepMind developed AI that can self-teach itself to become more efficient and reduce the energy used to cool data centres by 35%. Not only that, on October 6th, Google announced new features to its core products that will support users to make more sustainable decisions. For instance, by applying AI and data on energy use, Google added a new feature where customers can now not find the fastest route but the most eco-friendly, fuel-efficient route that reduces carbon emissions. Being able to see numbers allows customers to become more aware of the environmental impact they are making while travelling on vehicles. Moreover, IBM uses AI to improve the accuracy of weather forecasting by 30% which helps to better manage renewable energy plants, maximize production, and reduce carbon emissions. As such, the positives of AI as a tool for innovative solutions for protecting our environment outweigh the negative impact it leaves.
As shown above, AI is expected to be at the core of leading environmental sustainability and creating a sustainable future in the Digital Age. Conducting more studies and research on how AI can be used for social good as well as increasing accessibility of environmental data for AI development are crucial steps to take for building a sustainable future.