MACHINE LEARNING TECHNOLOGIES IN THE GREENTECH INDUSTRY: A CASE STUDY

As sustainability gains popularity and becomes more and more relevant in today’s economy, so does Artificial Intelligence and more specifically Machine Learning. New climate challenges arise, and the environment's health declines, stimulating new solutions and new approaches to be put into effect. This is what resides at the core of the GreenTech industry, which takes into consideration different technologies and tries to leverage them to challenge sustainability issues. Artificial intelligence and Machine Learning could play an important role in achieving this goal. The following work aims to highlight the importance that a Machine Learning approach can have on the GreenTech industry. This dissertation elaborates on the point of contact between these two trends, using a real-life business case as an example. The case revolves around an ML consulting agency and its client, a BlueTech company that produces data products regarding the seas’ health status. The company object of scrutiny operates in the context of the Sustainable Development Goals (SDGs) as it aligns with goals 13,14 and 17. The analysis of the case shines a light on the possibility of improving an early-stage BlueTech company with specialized Machine Learning consulting and implementation. To conclude his work the author reflects on the results of the case and the conflicting effects of the application of AI and ML. The outcome of the case study is positive as ML solutions improve the efficiency and scalability for the client. At the same time, the application of technologies such as AI and ML requires a lot of energy consumption, and it is therefore polluting. The duality of those technologies makes them beneficial and harmful to the environment at the same time.