This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.

E-14 F Reward Evaluation and Customization F-19 F.1 Load Shifting Penalty ( LS F-19 F.2 Default Reward Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F-19 F.3 Customization of Reward Formulations . . . . . . . . . . . . . . . . . . . . . . . Current Workload - The current workload level, which includes both flexible and non-flexible components. The data center modeled is illustrated in Figure 1. The hot air exits the cabinets and returns to the CRAH via the ceiling.

Artificial intelligence (AI) has enabled remarkable breakthroughs in numerous areas of critical importance, including tackling global challenges such as climate change. On the other hand, many AI models, especially large generative ones like GPT-4, are trained and deployed on energy-hungry servers in warehouse-scale datacenters, accelerating the datacenter energy consumption at an unprecedented rate.25 As a result, AI's carbon footprint has been undergoing scrutiny, driving the recent progress in AI carbon efficiency.24,31 However, AI's water footprint--many millions of liters of freshwater consumed for cooling the servers and for electricity generation--has largely remained under the radar and keeps escalating. If not properly addressed, AI's water footprint can potentially become a major roadblock to sustainability and create social conflicts, as freshwater resources suitable for human use are extremely limited and unevenly distributed.

In recent years, Large Language Models (LLM) such as ChatGPT, CoPilot, and Gemini have been widely adopted in different areas . As the use of LLMs continues to grow, many efforts have focused on reducing the massive training overheads of these models. But it is the environmental impact of handling user requests to LLMs that is increasingly becoming a concern. Recent studies estimate that the costs of operating LLMs in their inference phase can exceed training costs by 25 per year. A s LLMs are queried incess antly, the cumulative carbon footprint for the operational phase has been shown to far exceed the footprint during the training phase. Further, estimates indicate that 500 ml of fresh water is expended for every 20 - 50 requests to LLMs during inference. To address these important sustainability issues with LLMs, we propose a novel framework called SLIT to co - optimize LLM quality of service (time - to - first token), carbon emissions, water usage, and energy costs . The framework utilizes a machine learning (ML) based metaheuristic to enhance the sustainability of LLM hosting across geo - distributed cloud datacenters. Such a framework will become increasingly vital as LLMs proliferate.

Amazon, Microsoft and Google are operating datacentres that use vast amounts of water in some of the world's driest areas and are building many more, an investigation by SourceMaterial and the Guardian has found. With Donald Trump pledging to support them, the three technology giants are planning hundreds of datacentres in the US and across the globe, with a potentially huge impact on populations already living with water scarcity. "The question of water is going to become crucial," said Lorena Jaume-Palasí, founder of the Ethical Tech Society. "Resilience from a resource perspective is going to be very difficult for those communities." Efforts by Amazon, the world's largest online retailer, to mitigate its water use have sparked opposition from inside the company, SourceMaterial's investigation found, with one of its own sustainability experts warning that its plans are "not ethical".

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters ($hm^3$) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

In July, Elon Musk made a bold prediction: that his artificial intelligence startup xAI would release "the most powerful AI in the world," a model called Grok 3, by this December. The bulk of that AI's training, Musk said, would happen at a "massive new training center" in Memphis, which he bragged had been built in 19 days. But many residents of Memphis were taken by surprise, including city council members who said they were given no input about the project or its potential impacts on the city. And in the months since, an outcry has grown among community members and environmental groups, who warn of the plant's potential negative impact on air quality, water access, and grid stability, especially for nearby neighborhoods that have suffered from industrial pollution for decades. These activists also contend that the company is illegally operating gas turbines.

This study examines how artificial intelligence (AI), especially Reinforcement Learning (RL), can be used in farming to boost crop yields, fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate change and limited agricultural knowledge, we use Partially Observable Markov Decision Processes (POMDPs) with a crop simulator to model AI agents' interactions with farming environments. We apply deep Q-learning with Recurrent Neural Network (RNN)-based Q networks for training agents on optimal actions. Also, we develop Machine Learning (ML) models to predict N$_2$O emissions, integrating these predictions into the simulator. Our research tackles uncertainties in N$_2$O emission estimates with a probabilistic ML approach and climate variability through a stochastic weather model, offering a range of emission outcomes to improve forecast reliability and decision-making. By incorporating climate change effects, we enhance agents' climate adaptability, aiming for resilient agricultural practices. Results show these agents can align crop productivity with environmental concerns by penalizing N$_2$O emissions, adapting effectively to climate shifts like warmer temperatures and less rain. This strategy improves farm management under climate change, highlighting AI's role in sustainable agriculture.

The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water (withdrawal and consumption) footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand may be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4 -- 6 Denmark or half of the United Kingdom. This is very concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models' runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.