Assessing carbon budgets and reduction pathways in different income levels with neural network forecasting

Understanding carbon emissions across income levels is vital to limiting global warming to below 1.5 °C and 2 °C, as the Paris Agreement outlines. Unequal emission patterns can affect global remaining carbon budgets, necessitating differentiated strategies. This paper introduces a unique perspective to forecasting carbon emissions and determining the remaining carbon budget relative to income levels and associated socioeconomic factors. Leveraging Pearson correlation analysis and Bayesian-optimised Artificial Neural Network methods, the study examines emissions and carbon budgets of thirty countries, representing 82 % of global carbon emissions in 2022, under a business-as-usual scenario across various income levels. Findings reveal distinct patterns, with high-income countries experiencing declining emissions while middle- and low-income countries anticipate increases. High-income countries are projected to deplete their 2.0 °C budgets later than middle- and low-income countries, with an estimated depletion around 2052 and 2046–2048, respectively. Proposed mitigation and adaptation strategies tailored to income levels include investment in advanced technologies for high-income countries, promoting energy efficiency and renewable energy transition for middle-income countries, and expanding clean energy access for low-income countries. This study advocates the importance of integrating income levels and socioeconomic factors into climate policymaking, offering insights for policymakers, environmentalists, and businesses to tailor interventions and promote equitable climate action.