Analyzing the Impact of GDP on CO2 Emissions and Forecasting Africa’s Total CO2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory

Abstract

The amount of total carbon dioxide (CO2) emissions emitted into the environment threatens both human and global ecosystems. Based on this background, this study first analyzed the relationship between gross domestic product (GDP) and CO2 emissions in five West African countries covering the period of 2007–2014 based on a panel data model. Our causality analysis revealed that there exists a unidirectional causality running from GDP to CO2 emissions. Second, after establishing the nexus between GDP and CO2 emissions, we forecast Africa’s CO2 emissions with the aim of projecting future consumption levels. With the quest to achieve climate change targets, realistic and high accuracy total CO2 emissions projections are key to drawing and implementing realizable environmentally-friendly energy policies. Therefore, we propose a non-assumption driven forecasting technique for long-term total CO2 emissions. We implement our bidirectional long short-term memory (BiLSTM) sequential algorithm formulation for both the testing stage (2006–2014) and forecasting stage (2015–2020) on Africa’s aggregated data as well as the five selected West African countries employed herein. We then propose policy recommendations based on the direction of causality between CO2 emissions and GDP, and our CO2 emissions projections in order to guide policymakers to implement realistic and sustainable policy targets for West Africa and Africa as a whole.