• Energy Research
• 2016-2025close

Adopting a circular economy (CE) can play a role in achieving economic sustainability for all countries. Material and production waste must be recycled to make better use of limited resources. Developments in the CE need to transition linear economies into circular ones. Although the CE has a role in reaching economic sustainability, few studies have investigated the effect of transitioning to a CE in emerging economies. Thus, it is critical to examine the effect of circular economic influences on economic growth. This paper analyses particular indicators of the CE in the Gulf Cooperation Council (GCC) countries. The analysis employs econometric techniques such as unit root tests, random-effect models, and the autoregressive distributed lag (ARDL) model to examine different components, including environmental, social, and economic. Panel data are used to determine the dependency of circular economic factors on economic growth in GCC countries. The data was collected from the World Bank database covering the years 2000 to 2020. The paper is based on the analysis of the CE filed in GCC countries and intends to contribute to the studies in the field. The results gained from the GCC situation are valuable for both emerging and developing countries looking to include sustainable development measures in their policies and regulations. The findings highlight the importance of the CE to sustainability within GCC countries. This investigation of CE indicators based on the results of the economic model contributes to the empirical literature on the transition to a CE in emerging and developing countries.

The present study undertakes a comprehensive assessment of two predictive models, namely Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP), with a specific emphasis on their effectiveness in predicting oil prices, particularly those of the Petroleum Exporting Countries (OPEC). In this study, three fundamental statistical measures are utilized: The Symmetric Mean Absolute Percentage Error (SMAPE), the Mean Squared Error (MSE), and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the LSTM model regularly surpasses the MLP model in the three benchmarks. In particular, the LSTM model demonstrates lower values for SMAPE, MSE, and MAPE, indicating higher prediction accuracy. The decreased error scores linked to the LSTM model highlight its improved capacity for precise oil price prediction in comparison to the MLP model. These results signify a notable progress in the use of machine learning techniques for predicting OPEC oil prices. Moreover, this study provides invaluable perspectives for OPEC management, policymakers, and organizations focused on oil price fluctuations, therefore contributing to the wider endeavour of enhancing the stability and economic sustainability of the oil pricing system in OPEC countries. The consequences of the study include the promotion of a pricing system that facilitates the achievement of economic and social development goals in these countries.