From Data to Sustainability
Authors: Nagar Mayura Rupesh 
Published: December 2024

Thus, AI technologies stand out as an enabler in the constantly shifting context of sustainability. This chapter, which follows the general topic of “Beyond the AI Hype: Applying AI Technologies for Sustainability,” closely analyses and reflects upon a number of carefully chosen real-life cases which show how sustainability can be promoted with the help of AI across various fields. In this chapter, eight real-time cases are elaborated implementing the insights with area identification and discussion. The exploration starts with the energy division then segregating Google's DeepMind for Energy Optimization as well as IBM's Green Horizon for Air Quality Management. These case study expose the significant role of AI in the development of energy efficiency and better quality of atmosphere with real and live experience and best practices. The chapter then turns to environmental conservation in order to unpack The Ocean Clean-up's application of AI for Ocean Plastic Detection and Microsoft AI for Earth Program. These cases bring focus on how AI has been deployed to tackle various environmental issues affecting the world today such as removing plastics from the oceans to tracking deforestation and loss of species, among others. The narrative widens to include the sustainable use of transportation going to Tesla's Autopilot for Energy Efficient Driving and Alibaba's City Brain for Traffic Management in Cities. Both cases, illustrate how AI algorithms can improve energy use of automobiles and dynamics of traffic flow in urban environments and hence contribute towards more environmentally friendly transport solutions. This leads into the topics of renewable energy system with Siemens Gamesa's AI-Enhanced Wind Turbine Operations, illustrating how AI can improve the operational efficiency and maintenance of wind turbines. Connected Conservation for Wildlife Protection by Cisco unrolls as a testimony of the capability of AI in fighting wildlife poaching and protecting the vulnerable species. The chapter is rounded off with Alumni's Collaboration in Sustainability Projects looking at how and to what extent diverse stakeholders get involved in sustainability once they are out of school. Taken together, these cases depict the complex aspects of the use of AI for sustainability. Starting from energy conservation and extending to environmental surveillance, managing, transportation, renewable energy source, and wildlife surveillance, every case illustrates a facet of the worthful application of AI technologies for environmental good. The findings and implications presented in each of the case studies provide policy makers, practitioners, academics and specialists and environmentalists with ideal types and ideas of the opportunities that AI can provide for establishing a more sustainable and more resilient future. With such trends featuring constant evolution of a sustainability paradigm, the integration of what is AI technologies has emerged as the next frontier of positive change. Under the overarching theme of ‘achieving sustainability with AI technologies', this chapter approaches the organization of the text and the presentation of the material quite systematically, presenting eight selected successful case studies. All these examples can be considered as success stories, the stories that describe how AI can be used for sustainability in numerous fields. The exploration begins in the energy subdomain where the inner functioning of Google's DeepMind for Energy Optimization and IBM's Green Horizon for Air Quality Management is examined. These two cases unroll the deep worth of AI; illustrating its ability to manage power usage and improve the quality of air. Implementation in real life presents the actual result, provide lessons on the integration of sustainable practices while working on large organizational systems. By changing the contemporaneity to environmental conservation, the account explains the creative application of The Ocean Clean-up in deploying AI for Ocean Plastic Detection and Microsoft's AI for Earth Program. These cases highlight how AI can play a central part in managing global environmental problems ranging from removing different types of plastics from the marine environments to tracking the progression of deforestation and changes to biological diversity. The sophistication of the technologies demonstrated in the above examples can be seen to therefore indicate AI's ability to help global threats in regard to climatic change. The expansion broadens its scope to the sustainable transportation discussing Tesla's Autopilot for Energy-Efficient Driving as well as Alibaba's City Brain for Traffic Control in Urban Area. In these examples, the chapter reveals that the AI algorithms play the key role in enhancing driver's energy spend and coordinating flow of traffic in urban environment. These applications do not only help make transport green but also are a testimony of how AI is set to transform mobility in future. Moving deeper to renewable energy sector the product named Siemens Gamesa's AI Enhanced Wind Turbine Operations appears to be the most prominent. This particular case shows the potential of how the integration of AI based solutions can enhance the functionality of WTG and support increased reliability of the Renewable Power sources. Based on Cisco Connected Conservation for Wildlife Protection, one can speak about the presence of a ray of hope in the framework of wildlife saving. In this case, it is revealed how great extents of AI and IoT can help in fighting poaching and saving endangered species. The successful implementation demonstrates that AI could be a very useful partner in the process of wildlife protection. The climax is presented in the form of Alumni's Collaboration in Sustainability Projects that discuss various partnership ventures in sustainability. This section focuses on teamwork in striving to build sustainable community projects after the conclusion of graduation illustrating that everyone has a part in making the world a better place. Altogether, all the described cases provide a vivid picture of how AI contributes to the accomplishment of sustainability objectives. This provides the six case studies which encompass energy efficiency and environmental moderation, conservation, transport and renewable energy, and environmentalist technologies for protection of creatures. The policy recommendations, successes and failures presented in each of the case studies present a detailed account that should prove useful to policymakers, industry practitioners, academics, and enthusiasts of the environment to promote a richer appreciation of AI's capacity to meld a better world. This chapter alone is a manual on how AI can be successfully adopted and at the same time, an invitation for organisations to further the pursuit and collaboration in the pursuit of global sustainability.