School of Creative and Cultural Business, Robert Gordon University, Aberdeen, UK.
Environ Monit Assess. 2024 Feb 3;196(3):231. doi: 10.1007/s10661-024-12388-6.
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.
在全球范围内，各国政府正在制定政策和战略，以减少碳排放，应对气候变化。监测政府减少碳排放政策的影响，可以显著提高我们应对气候变化和实现减排目标的能力。在这方面，一个有前途的领域是人工智能（AI）在碳减排政策和战略监测中的作用。虽然研究人员已经探索了 AI 在从传感器、卫星和社交媒体等各种来源获取的数据中的应用，以确定减排领域，但 AI 在跟踪政府减排计划效果方面的应用一直受到限制。本研究提出了一种基于长短期记忆（LSTM）和统计过程控制（SPC）的 AI 框架，用于监测碳排放的变化，使用英国年度 CO2 排放（人均）数据，涵盖 1750 年至 2021 年期间。本文使用 LSTM 为英国的碳排放量特征和行为开发了一个替代模型。正如我们的实验所观察到的，LSTM 在预测 CO2 排放方面比 ARIMA、指数平滑和前馈人工神经网络（ANN）具有更好的预测能力，在每年的预测范围内。然后使用记录的排放数据与替代过程的偏差，使用 SPC（特别是 Shewhart 个体/移动范围控制图）分析这些行为的变化和趋势。结果显示，1990 年代中期至 2021 年期间存在一些可归因的变化，这与英国政府在此期间内降低碳排放的一些重要承诺相关。本文提出的框架可以帮助识别与一个国家正常 CO2 排放相比出现重大偏差的时期，这可能是政府的碳减排政策或活动导致 CO2 排放量发生变化的结果。
