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Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions. [ABSTRACT FROM AUTHOR]

Over the last several years, the United Kingdom has seen a wave of environmental movements demanding action on the climate crisis. While aligned in their goals, the groups undertaking this activism often diverge on the question of tactics. One such divergence occurred in January 2023, when Extinction Rebellion (XR) declared "We Quit", ending actions that were disruptive to the general public. Peer groups Just Stop Oil and Animal Rising continued disruptive actions, viewing them as the best way to gain media coverage for their causes. Despite the urgency of addressing climate change and the growing prominence of direct action in British life, little research has examined how the news media covers and reacts to different climate actions. News media plays a vital role in influencing the public's perception of the climate crisis and "appropriate" responses. We assembled a unique dataset of British news coverage of climate actions over a 7 month period, covering both before and after XR's "We Quit" statement. Our results reveal that conservative publications cover climate actions more unfavorably and more inaccurately than other publications. Legal actions are generally covered more favorably than illegal ones in both conservative and non-conservative outlets and receive more coverage. Actions that target industry attract more coverage than those that target other actors, while actions that target the public are covered more favorably than those that do not. These results contribute to the scholarly debates surrounding the interaction between social movements and news media, especially on how different strategies potentially influence the extent and affective nature of coverage. They have implications for strategies adopted by climate advocates, depending on whether their goal is merely to draw attention to an issue or if it is to generate positive coverage. [ABSTRACT FROM AUTHOR]