Determinants of energy consumption: The case of African oil exporting countries

Ogunsola, Akindele John; Tipoy, Christian K.

doi:10.1080/23322039.2022.2058157

Cited by 6 publications

(1 citation statement)

References 60 publications

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“…The input variables for demand forecasting include carbon emissions [49], income level [50], energy price [51], GDP [52], Population growth [53], urbanization [54], trade liberalization [55], inflation [53], FDI [56], financial development [52], energy sanction [22], and pandemic [24]. The R software (version of 4.2) and the libraries, including caret, e1071, ggplot, ggpubr, cluster, and gclus, were used for data analysis and model fitting.…”

Section: Methodsmentioning

confidence: 99%

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Zhu

2023

Sustainability

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Given the prevalence of the digital world, artificial intelligence (AI) stands out as one of the most prominent technologies for demand prediction. Although numerous studies have explored energy demand forecasting using machine learning models, previous research has been limited to incorporating either a country’s macroeconomic characteristics or exogenous elements as input variables. The simultaneous consideration of both endogenous and exogenous economic elements in demand forecasting has been disregarded. Furthermore, the stability of machine learning models for energy exporters and importers facing varying uncertainties has not been adequately examined. Therefore, this study aims to address these gaps by investigating these issues comprehensively. To accomplish this objective, data from 30 countries spanning the period from 2000 to 2020 was selected. In predicting oil demand, endogenous economic variables, such as carbon emissions, income level, energy price, gross domestic product (GDP), population growth, urbanization, trade liberalization, inflation, foreign direct investment (FDI), and financial development, were considered alongside exogenous factors, including energy sanctions and the COVID-19 pandemic. The findings indicate that among the input variables examined in demand forecasting, oil sanctions and the COVID-19 pandemic have had the most significant impact on reducing oil demand, while trade liberalization has proven to be the most influential factor in increasing oil demand. Furthermore, the support vector regression (SVR) model outperforms other models in terms of lower prediction error, as revealed by the error assessment of statistical models and AI in forecasting oil demand. Additionally, when comparing the stability of models in oil exporting and importing countries facing different levels of demand uncertainty, the SVR model demonstrates higher stability compared to other models.

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Section: Methodsmentioning

confidence: 99%

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Zhu

2023

Sustainability

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Asymmetric Effect of Green Energy and Economic Growth on the Environmental Deterioration and the Environmental Kuznets Curve Validation in MENA Countries

Wani,

Alamir,

Ghazwani

et al. 2024

Int. J Fin Econ

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This study aims at investigating the contextual factors surrounding the nexus between economic development and environmental degradation by utilising the Environmental Kuznets Curve (EKC) hypothesis and incorporating CO2 emissions indicator. The environmental quality, energy source, economic growth nexus evaluation is heavily dependent on the level of economic growth. The study examines impact of different levels of economic expansion following an Environmental Kuznets Curve (EKC) hypothesis across Middle East and North Africa (MENA). Additionally, the present study examines the asymmetrical relationship that exist between economic growth and green energy, and the environmental quality. The study employs panel data ranging from 1996 to 2020 in the context of MENA nations. Several econometric tests were employed, second generation unit root tests, CIPS and CADF, Westerlund cointegration test. This study extends prior studies by applying the Panel Non‐Linear Autoregressive Distributed Lag (PNARDL). The Westerlund test validates a long run association among the variable of the study. The EKC hypothesis is validated in the short run for the MENA countries. It also reveals that a positive and negative shocks to the green energy decreases CO2 emissions. The positive shock to GDP results in an increase in CO2 emissions. Moreover, the study revealed a negative and significant relationship between FDI and CO2 emission. A positive and significant relationship was found between trade openness and CO2 emissions. Our findings suggest that the MENA nations should switch to green energy sources to lessen the impact of non‐renewable energy consumption on the environment and promote sustainable development, since non‐renewable energy is believed to have the greatest impact on economic activity.

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Environmental pollution effects of economic, financial, and industrial development in OPEC: comparative evidence from the environmental Kuznets curve perspective

Demiral

Haykır

AKTEKİN

2023

Environ Dev Sustain

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Determinants of energy consumption: The case of African oil exporting countries

Cited by 6 publications

References 60 publications

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Asymmetric Effect of Green Energy and Economic Growth on the Environmental Deterioration and the Environmental Kuznets Curve Validation in MENA Countries

Environmental pollution effects of economic, financial, and industrial development in OPEC: comparative evidence from the environmental Kuznets curve perspective

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