Journal of Business Economics and Management

A new hybrid approach to the impact of renewable energy consumption on economic growth: sectoral differences in European Union countries

Anca Mehedintu, Georgeta Soava — Fri, 27 Sep 2024 09:55:31 +0300

The current energy crisis has shown all states that energy from renewable sources can be a determining factor in the states’ sustainable development. Several papers have studied the relationship between renewable energy consumption and economic development, finding various situations, but there is no consensus. Thus, this study aims to first investigate the causal relationship between economic growth and total and sectoral renewable energy consumption (European Union and each Member State, for 2004–2020) by testing various linear and non-linear regressions to choose the fit model. Second, the investigation extends to analysing the impact of renewable energy consumption by sector on economic development. A hybrid approach is used, namely structural equation modelling and artificial neural networks. The study findings indicate the effect and the meaning (directly or inversely) exerted by the three sectoral components on economic growth, with different intensities from one country to another. There is a significant influence on the consumption of renewable energy in the heating and cooling sectors and transport on gross domestic product at the European Union level and for most member states. Based on the obtained results, a series of theoretical, practical, and political implications are provided.

Enhancing environmental sustainability in Asian textile supply chains: insights from agile practices and mediating variables

Fri, 27 Sep 2024 09:59:50 +0300

Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm

Fri, 27 Sep 2024 13:15:07 +0300

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.