Emmanuel Bannor B. and Alex O. Acheampong
This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA.
Abstract
Purpose
This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA.
Design/methodology/approach
The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model.
Findings
The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India.
Originality/value
This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.
Details
Keywords
Ernest Kissi, Theophilus Adjei-Kumi, Edward Badu and Emmanuel Bannor Boateng
Tender price remains an imperative parameter for clients in deciding whether to invest in a construction project, and it serves as a basis for tender price index (TPI…
Abstract
Purpose
Tender price remains an imperative parameter for clients in deciding whether to invest in a construction project, and it serves as a basis for tender price index (TPI) manipulations. This paper aims to examine the factors affecting tender price in the construction industry.
Design/methodology/approach
Based on the literature review, nine independent constructs and one dependent construct relating to tender pricing were identified. A structured questionnaire survey was conducted among quantity surveyors in Ghana. Partial least squares structural equation modelling (PLS-SEM) examined the influences of various constructs on tender price development (TPD) and the relationships among TPD and TPI.
Findings
Results showed that cultural attributes, client attributes, contractor attributes; contract procedures and procurement methods; consultant and design team; external factors and market conditions; project attributes; sustainable and technological attributes; and TPI have a positive influence on tender price, whereas fraudulent attributes exert a negative influence.
Practical implications
The findings offer construction professionals broader understanding of factors that affect tender pricing. The results may be used in professional decision-making in the pricing of construction projects, as they offer clearer causal relations between how each construct will influence pricing.
Originality/value
This study adds to the body of construction pricing knowledge by establishing the relationships and degree of influences of various factors on tender price. These findings provide a valuable reference for practitioners.
Details
Keywords
Ifeyinwa Juliet Orji and Francis I. Ojadi
Extreme weather events are on the rise around the globe. Nevertheless, it is unclear how these extreme weather events have impacted the supply chain sustainability (SCS…
Abstract
Purpose
Extreme weather events are on the rise around the globe. Nevertheless, it is unclear how these extreme weather events have impacted the supply chain sustainability (SCS) framework. To this end, this paper aims to identify and analyze the aspects and criteria to enable manufacturing firms to navigate shifts toward SCS under extreme weather events.
Design/methodology/approach
The Best-Worst Method is deployed and extended with the entropy concept to obtain the degree of significance of the identified framework of aspects and criteria for SCS in the context of extreme weather events through the lens of managers in the manufacturing firms of a developing country-Nigeria.
Findings
The results show that extreme weather preparedness and economic aspects take center stage and are most critical for overcoming the risk of unsustainable patterns within manufacturing supply chains under extreme weather events in developing country.
Originality/value
This study advances the body of knowledge by identifying how extreme weather events have become a significant moderator of the SCS framework in manufacturing firms. This research will assist decision-makers in the manufacturing sector to position viable niche regimes to achieve SCS in the context of extreme weather events for expected performance gains.