Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Ezenkwu, Chinedu Pascal; Cannon, San; Ibeke, Ebuka

Abstract

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.

Citation

EZENKWU, C.P., CANNON, S. and IBEKE, E. 2024. Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. Environmental monitoring and assessment [online], 196(3), article number 231. Available from: https://doi.org/10.1007/s10661-024-12388-6

• Journal Article Type: Article
• Acceptance Date: Jan 20, 2024
• Online Publication Date: Feb 3, 2024
• Publication Date: Mar 31, 2024
• Deposit Date: Jan 22, 2024
• Publicly Available Date: Jan 22, 2024
• Journal: Environmental monitoring and assessment
• Print ISSN: 0167-6369
• Electronic ISSN: 1573-2959
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 196
• Issue: 3
• Article Number: 231
• DOI: https://doi.org/10.1007/s10661-024-12388-6
• Keywords: Carbon emissions; LSTM; Statistical process control; Artificial intelligence; Climate change; Energy policy; Deep learning; ARIMA; Exponential smoothing; ANN
• Public URL: https://rgu-repository.worktribe.com/output/2217625