Donald Trump signed four executive orders on Tuesday aimed at reviving coal, the dirtiest fossil fuel that has long been in decline, and which substantially contributes to planet-heating greenhouse gas emissions and pollution. Environmentalists expressed dismay at the news, saying that Trump was stuck in the past and wanted to make utility customers "pay more for yesterday's energy". The US president is using emergency authority to allow some older coal-fired power plants scheduled for retirement to keep producing electricity. The move, announced at a White House event on Tuesday afternoon, was described by White House officials as being in response to increased US power demand from growth in datacenters, artificial intelligence and electric cars. Trump, standing in front of a group of miners in hard hats, said he would sign an executive order "that slashes unnecessary regulations that targeted the beautiful, clean coal".

In this paper, we present a novel approach to coal mining question answering (QA) using large language models (LLMs) combined with tailored prompt engineering techniques. Coal mining is a complex, high-risk industry where accurate, context-aware information is critical for safe and efficient operations. Current QA systems struggle to handle the technical and dynamic nature of mining-related queries. To address these challenges, we propose a multi-turn prompt engineering framework designed to guide LLMs, such as GPT-4, in answering coal mining questions with higher precision and relevance. By breaking down complex queries into structured components, our approach allows LLMs to process nuanced technical information more effectively. We manually curated a dataset of 500 questions from real-world mining scenarios and evaluated the system's performance using both accuracy (ACC) and GPT-4-based scoring metrics. Experiments comparing ChatGPT, Claude2, and GPT-4 across baseline, chain-of-thought (CoT), and multi-turn prompting methods demonstrate that our method significantly improves both accuracy and contextual relevance, with an average accuracy improvement of 15-18\% and a notable increase in GPT-4 scores. The results show that our prompt-engineering approach provides a robust, adaptable solution for domain-specific question answering in high-stakes environments like coal mining.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network weights. The proposed method greatly extends the methodology and applicability of statistical network models. Furthermore, it is scalable to large and complex networks in large-scale environmental studies and geoscientific research. The power of our proposed methods is demonstrated in simulation studies.

Can coal make a comeback under President Trump? In her old life, it was not unusual for Annie Evans to find herself standing in the pouring rain outside of a coal mine at three in the morning, staring down a broken piece of equipment. A heavy maintenance superintendent for a surface coal mine in Elgin, Texas, Evans was responsible for figuring out how to patch or replace outdated parts of a field delivery system that ferried coal from the mine to a plant. Each minute of downtime could cost the company as much as $170. Now the third-generation coal miner gets her adrenaline rush sitting indoors on a soft swivel chair, fixing code on a computer screen.