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.

The world has not been able to achieve minimum greenhouse gas emissions in buildings’ energy consumptions because the energy and emissions optimization techniques have not been fully utilized. Thermal comfort is one of the most important issues for both residential and commercial buildings. Out of the 40% of global energy consumed by buildings, a large fraction is used to maintain their thermal comfort. In this study, a comprehensive review of the recent advancements in building energy conservation and efficiency application is presented based on existing high-quality research papers. Additionally, the retrofit of the heating/cooling and hot water system for an entire community in Cyprus is presented. This study aims to analyze the technical and environmental benefits of replacing existing electric heaters for hot water with heat pump water heating systems and the use of heat pump air conditioners for thermal comfort in place of the existing ordinary air conditioners for space heating and cooling. One administrative building, 86 apartments (including residential and commercial) buildings, and a restaurant building is retrofitted, and the feasibility of the project is determined based on three economic indicators, namely; simple payback period (SPP), internal rate of return (IRR), and net present value (NPV). The electrical energy required by the hot water systems and the heating/cooling system is reduced by 263,564 kWh/yr and 144,825 kWh/yr, respectively. Additionally, the retrofit project will reduce Cyprus’ CO₂ emission by 121,592.8 kg yearly. The SPP, IRR, and NPV for the project show that the retrofit is economically feasible.

In order to implement sustainable economic policies, realistic and high accuracy demand projections are key to drawing and implementing realizable environmentally-friendly energy policies. However, some core energy models projections depict considerably high forecast inaccuracies in their previous projections. The inaccuracies are due to the massive assumption-driven variables whose assumptions and scenarios typically deviate from their realized levels. Here, we propose a high-accuracy assumption-free own-data-driven technique that utilizes zero of the traditional determinants as well as assumptions or scenarios for sectorial energy demand forecasting; and implement it in the United States (U.S.). The results show that the forecast accuracy of our gated recurrent network presents an enormous improvement on Annual Energy Outlook 2008 forecast projections. With evidence that our proposed sequential algorithm outperformed Annual Energy Outlook 2008 forecast projections, our proposed algorithm will guide policymakers in making sustainable energy-related policies in the near future. Although future realized consumption levels are unknown, we present our estimated projections along with Annual Energy Outlook 2018 projections to inform policymakers on future energy demands for the commercial sector, industrial sector, residential sector, and transportation.