A review of the use of machine learning techniques in eco-innovation research

Machine learning is a powerful tool used across research all over the world. Machine learning algorithms are a form of artificial intelligence that allows more accurate predictions of causal conditions of all kinds, being able to analyze complex data samples beyond what a human could do. Machine learning mimics human reasoning by creating a neural network, and this has proven to be a useful technique to solve complex problems. The thread of climate change is one of the most complex problems that humanity is currently facing. On one hand, we need the industries and the market to continue to function to guarantee covering the needs of the population, and its continued development. On the other hand, this development must guarantee the conservation of the planet and its habitability conditions, which are essential for the continued existence of a world to be left to future generations. Reducing the harmful effects that business-related activities have on the natural environment is key to guarantee a sustainable future, and this done, among other elements, through eco-innovation techniques. Therefore, both machine learning and eco-innovation are striving topics across researchers nowadays, but: Are these two topics linked to each other? Is machine learning used as a tool to support a better understanding of eco-innovation (i.e., environmental innovation)? This review aims to understand what is the role that machine learning has in the context of eco-innovation. Results show that machine learning is not a widely used technique in the field of eco-innovation research and that there is a wide spectrum of research in which machine learning could be used in the future alongside the increasing research linked to eco-innovation.