## 1 Introduction  
The mining and metals industry is one of the building blocks of the fourth industrial revolution. Raw materials are the indispensable foundation for a future digital society that relies on information and communication technologies at home and work, as well as for education and recreation [1, 2, 3]. Despite the increasing impact of circularity, recycling, reusing and reinvesting in raw materials will not be enough to meet consumer demands and industrial needs [4, 5]. The challenge is enormous. Concretely, the 2020 Communication from the European Commission on Critical Raw Materials Resilience [6] indicates for batteries for electric vehicles and energy storage alone, up to 18 times more lithium and 5 times more cobalt will be required in 2030, and almost 60 times more lithium and 15 times more cobalt will be required in 2050, in relation to the current supply to the entire EU economy. However, mining still poses serious and highly specific threats to biodiversity. Projections suggest that demand will grow for manifold mining metals such as cobalt, nickel or copper, shifting mining operations towards more dispersed and biodiverse areas [7, 8]. In this scenario, mining companies urgently need to adapt their business models to become more sustainable, environmentally protective and resilient in the transition towards a low-carbon economy. For this reason, the sector is gearing up for innovation and technological transformation. Automation, robotics, machine learning, advanced analytics,digital twin, predictive maintenance, the industrial internet of things (IIoT) and modern data architecture (including cloud computing) can help the industry to achieve this digital transformation [9; 10; 11]. Importantly, artificial intelligence (AI) could make the mining and metals industry more ethical and sustainable [12; 13; 14]. It may also help to reduce the industry's environmental footprint, moving workers out of mines, reducing costs and risks. At the same time, mining productivity can experience important gains, especially at a point in time where ore grades are declining, water scarcity has threatened to strand assets, and exploitation licenses have become extremely difficult to obtain. Recent research and business developments show that achieving sustainable change requires more than a focus on technology [15; 16; 17; 18]. In particular, one of the key challenges is to harness digital analytics by developing and implementing AI and machine learning models that effectively solve environmental, productivity and supply change problems. This study aims to deepen the manner in which environmental impact assessment (EIA) results are determined. EIA is one of the principal tools used globally for sustainable mining [19; 20; 21]. It involves the application of different methodologies to evaluate the environmental impact of mining activities in the area of concern, supporting decision making ahead of new interventions. In essence, the EIA is a highly comprehensive tool that reflects adverse impacts in a concise manner, using typically an index [22; 23]. The main advantage of the index is to gather information of manifold variables, which represent different environmental factors, evaluating the feasibility of the project and the possible need to implement mitigation measures. On the negative side, all these estimated outcomes are traditionally conditioned by mining engineers and environmental experts who evaluate the area of study and provide their qualitative and quantitative input to the index model. In this study, the main goal is to introduce an innovative AI-based approach for the construction of EIA indexes that accurately reflects and takes into account the relationships between the different environmental factors, finding relevant patterns in the data and minimizing the influence of human bias.