ESG stands for Environmental, Social, and Governance factors, often used to measure sustainability. Our goal from this project is to evaluate both big and small companies on these criteria, giving a broader view of their sustainable practices. The current focus on large public companies overlooks smaller entities, limiting a holistic understanding of sustainability impacts. Key takes here for us are the lack of data and scoring.

Some companies claim to be green without truly being sustainable, a practice called greenwashing. Our goal is to create a solution that will detect and highlight these misleading claims, promoting genuine transparency.

ESG for smaller buyers, sellers, factories farms, and SMEs and digital identity
ESG criteria or the lack of this monitoring are important considerations for businesses of all sizes, including smaller buyers, sellers, factories, farms, and SMEs (small to medium-sized enterprises). However, implementing ESG practices and monitoring them can be a challenge for smaller entities due to limited resources. Digital identity can play a pivotal role in this context

ESG criteria are increasingly central to the investment decisions of global asset managers. They consider the sustainability and ethical impact of investments. Yet, many globally traded assets lack comprehensive ESG integration, which poses significant risks and challenges, especially for achieving global targets such as net zero on goods, In short, you want to know if the bananas you purchased are organic and did not involve child labor, this is paramount to retail as well as the financiers funding the trade if we are to have a more conscious planet and save it from climate change.

AI has high energy consumption primarily because modern deep learning models have billions of parameters that require intensive computations, especially when processed using power-hungry GPUs and TPUs. Training these models involves multiple iterations over vast datasets, often for extended periods. Additionally, practices like redundancy, fine-tuning, and hyperparameter searches multiply the energy demands, making the overall consumption notably high.