The "green transition" towards renewable energy and industrial processes will require a significant increase in the consumption of metals and minerals, which will involve expanding mining operations. The article explores the use of AI to extract valuable resources and mitigate the negative impacts of mining on local communities. AI can aid companies and governments in monitoring mine site violations, issues in mineral processing, and raw material supply chains. An interactive Multi Objective Optimization (iMOO) approach that captures the linkages between investment decisions and environmental, social, and economic outcomes is being developed and piloted. By including outcomes along environmental and social criteria, iMOO can promote more sustainable decision making, while allowing for the consideration of complex data and its linkages. However, information equality and the transparency of those results and access to these tools for companies, communities, and governments is critical. Computing the Pareto fronts that include social and environmental sustainability criteria helps communication among decision-makers and can help in addressing ethical concerns around AI use.

Environmental, social, and governance (ESG) standards are becoming increasingly important for investors, consumers, and regulators. However, achieving a strong ESG reporting practice is challenging due to a data sourcing problem and a dynamic regulatory environment globally. Companies are turning to Large Language Models (LLMs) for potential solutions to overcome these challenges. LLMs can help with data collection and analysis, better ratings, and estimates and predictions. Regulations on ESG reporting have increased globally, and there is also increasing focus on the environmental implications of AI, as well as the potential of AI to benefit ESG. To apply AI safely, appropriate guidelines and guardrails must be in place.

The upcoming EU AI Act aims to protect fundamental rights, democracy, the rule of law, and environmental sustainability from high-risk AI while establishing Europe as a leader in AI. The Act includes provisions concerning the environmental impact and energy consumption of AI systems, such as improving the resource performance of AI systems, reporting and documenting energy consumption, and encouraging compliance with environmental sustainability rules. The Act also establishes regulatory sandboxes to promote innovation under specific conditions, including high-level protection of the environment and energy sustainability. The EU AI Office and Member States will work together to draw up codes of conduct for voluntary application of specific requirements, including minimizing the impact of AI systems on environmental sustainability. The Act also requires regular evaluation and review of environmental provisions, including standardization deliverables and voluntary codes of conduct. Providers of general-purpose AI models must provide detailed information on computational resources used for training and energy consumption. The Act anticipates the fast-paced advancements in AI and allows for exemptions to conformity assessments in specific situations that ensure environmental protection and benefit society overall. Compliance requires a proactive, iterative approach.

AI has both positive and negative implications on the environment. While the technology uses vast amounts of energy, it offers ways to expand sustainable practices if its power is harnessed in the right way. AI developers can reduce their environmental impact by using efficient hardware, reducing inference time, and locating data centers in cleaner energy regions. Opting for single-purpose LLMs for specific tasks and increasing transparency on measurements of energy output can also help. Despite the high use of energy, AI can expand sustainable practices and help achieve the UN Sustainable Development goals. Regulation targeting AI’s environmental impact is being developed in the US and EU. Large companies have also announced initiatives to tackle sustainability. Various jurisdictions globally have begun to develop regulation to address AI's environmental impacts.

The growing use of Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), has significant environmental implications due to the high amount of energy required for computing power. Emissions related to the IT sector, including data centers, cryptocurrency, and AI, are set to sharply increase after 2023, with AI projected to consume the energy equivalent of a country like Argentina or the Netherlands by 2027. The manufacturing of chips, the training phase, and the live computing LLMs perform to generate predictions or responses contribute significantly to their environmental impact. This issue is a growing concern for society, manufacturers, developers, and policymakers who must work together to mitigate AI's high energy usage.