To combat global warming and mitigate the risks associated with climate change, carbon capture and storage (CCS) has emerged as a crucial technology. However, safely sequestering CO2 in geological formations for long-term storage presents several challenges. In this study, we address these issues by modeling the decision-making process for carbon storage operations as a partially observable Markov decision process (POMDP). We solve the POMDP using belief state planning to optimize injector and monitoring well locations, with the goal of maximizing stored CO2 while maintaining safety. Empirical results in simulation demonstrate that our approach is effective in ensuring safe long-term carbon storage operations. We showcase the flexibility of our approach by introducing three different monitoring strategies and examining their impact on decision quality. Additionally, we introduce a neural network surrogate model for the POMDP decision-making process to handle the complex dynamics of the multi-phase flow. We also investigate the effects of different fidelity levels of the surrogate model on decision qualities.

There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. The rapid growth of artificial intelligence (AI) is reshaping our society in many ways, and climate change is no exception. This Perspective presents a framework to assess how AI affects GHG emissions and proposes approaches to align the technology with climate change mitigation.

Artificial Intelligence (AI) and Machine Learning (ML) can be used to tackle crucial issues like climate change and carbon emissions, which could bring humanity one step closer to achieving our sustainability goals. However, increased use of AI and ML technologies can also have an impact on greenhouse gas emissions which means creating a sustainable form of these technologies is key to the wider picture of climate consciousness. Recently, a group of researchers led by Professor Lynn H. Kaack at Berlin's Hertie School published a paper in the journal Nature Climate Change investigating how AI and ML technologies may impact greenhouse gas emissions – both positively and negatively – and what measures can be taken help to align AI/ML policy with climate change goals. The aim of the study is to establish how the emissions from AI/ML activities can be quantified in order to better understand how the increasing use of these technologies is influencing the climate. Climate change should be a key consideration when developing and assessing AI technologies.