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

 Bismark Ameyaw ^1,2,* and Li Yao ^1,2

¹ School of Management and Economics, University of Electronic Science and Technology of China

² Center for West African Studies, University of Electronic Science and Technology of China

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.

Keywords

CO₂ emissions; bidirectional long short-term memory (BiLSTM); Africa; West Africa; diversification of energy sources; climate change; forecasting

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