Abstract
This study conducts a comprehensive analysis of CO2 emissions trends from 1990 to 2020 across critical sectors including agriculture, buildings, electricity, industry, oil and gas, and waste. Leveraging a robust dataset of 35,000 observations, we explore emission patterns and their impact on climate change mitigation efforts. Employing advanced time series models—such as the Drift Method, Holt Linear Method, Damped Trend Method, and ARIMA—we forecast emissions and evaluate these models using RMSE, MAE, and MAPE to gauge their predictive accuracy. This research aims to assess the feasibility for various countries to meet the Paris Agreement’s goal of limiting global warming to 1.5 \(^\circ \)C, aligned with the IPCC AR6 report’s benchmarks for emissions peaking by 2025 and a 43% reduction by 2030. Our analysis reveals critical insights into emission trajectories, underscoring the urgency and practicality of achieving global climate objectives. The findings serve as a crucial guide for policymakers in crafting informed, sustainable development strategies.
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Najr, T., Aldo, C., Karamitsos, I., Kanavos, A., Modak, S. (2024). Net Zero Strategies: Empowering Climate Change Solutions Through Advanced Analytics and Time Series. In: Maglogiannis, I., Iliadis, L., Karydis, I., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024 IFIP WG 12.5 International Workshops. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-63227-3_19
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