Toward a sustainable future:Examining the interconnectedness among Foreign Direct Investment(FDI),urbanization,trade openness,economic growth,and energy usage in Australia

The energy demand in Australia is increasing with the industrialization and rapid economic growth.This study analyzed the relationships among the economic growth,Foreign Direct Investment(FDI),trade openness,urbanization,and energy usage in Australia based on the data from World Development Indicators(WDI)from 1972 to 2021.The results indicates that there is a cointegration among economic growth,FDI,trade openness,urbanization,and energy usage,which was traced through the autoregressivedistributed lag(ARDL).The Zivot-Andrews unit root test reveals that energy usage,economic growth,FDI,urbanization,and trade openness show significant structural breaks in 1993,1996,1982,2008,and 1994,respectively.The ARDL model shows that economic growth has a positive and significant effect on energy usage in the long-run(0.814)and short-run(0.809).Moreover,the results also show that FDI(0.028)and trade openness(0.043)have positive impacts on energy usage in the long-run.However,urbanization shows a negative and significant influence on energy usage in the long-run(–0.965).Then,the research demonstrates a unidirectional causation between energy usage and trade openness,with energy usage significantly causing trade openness.The current study endorses energy consumption policies and investment strategies for a paradigm shifting from a reliance on fossil fuels as the primary energy source to renewable energy sources.These findings have profound implications for sustainable energy usage.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Droop Control for Micro-grid Operations Including Generation Cost and Demand Side Management

A new cost-based droop control method based upon generation cost and demand side cost management of the microgrid is proposed in this paper.At present,many droop control methods have been developed based on either the power rating or the generation cost of the distributed generation(DG)unit,without consideration of the demand side participation in the operation and control.This exclusion might not be appropriate,if different types of consumers are connected in the micro-grid systems.This study proposes a droop control method considering both DG and load operating cost characteristics in order to minimize the generation cost of the micro-grid.