Published Papers

Highlights

• Evaluating energy poverty across a sample of 32 economies from 2004 to 2021.
• Data from the EU-SILC database and Eurostat were utilised.
• Fixed-effect regression models were used to identify factors influencing energy poverty at the household level.
• Strategies for addressing energy poverty include enhancing housing conditions and lowering electricity expenses.
• Households that are low-income, smaller in size, and in poor condition are more vulnerable to energy poverty.

Abstract

The domain of energy poverty is increasingly recognised as a multifaceted global challenge stemming from limited income, high energy costs, and inefficient housing. The issue affects different social groups and regions unevenly, even within Europe. This paper investigates energy poverty across 32 economies, including EU member states and several non-EU European countries, over the period from 2004 to 2021. By analysing micro-level data from the EU-SILC database and Eurostat, the study identifies that low-income households, smaller households, and those living in overcrowded conditions are particularly vulnerable to energy poverty. Interestingly, the research finds that renewable energy does not contribute to alleviating energy poverty in Europe. Based on these results, the study calls for immediate policy measures to improve housing conditions and lower electricity costs, especially for economically disadvantaged households, to effectively address energy poverty.

Following the importance of gold in the global economy and the high interest that has attracted recently, the objective of this paper is twofold: to predict the price of gold by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and compare its forecasting accuracy with various time-series forecasting methods and the 'Buy and Hold' (B&H) strategy. The results show that the ANFIS's accuracy is far superior to the performance of all compared methods and therefore ANFIS demonstrates the potential of neuro fuzzy-based modelling for predicting the gold's price.

We show that the application of flexible semi-parametric statistical techniques enables significant improvements in model fitting of macroeconomic models. As applied to the explanation of the past economic growth (since 1900) in US, UK and Japan, the new results demonstrate quite conclusively the non-linear relationships between capital, labour and useful energy with economic growth. They also indicate that output elasticities of capital, labour and useful energy are extremely variable over time. We suggest that these results confirm the economic intuition that growth since the industrial revolution has been driven largely by declining energy costs due to the discovery and exploitation of relatively inexpensive fossil fuel resources. Implications for the 21st century, which are also discussed briefly by exploring the implications of an ACEGES-based scenario of oil production, are as follows: (a) the provision of adequate and affordable quantities of useful energy as a pre-condition for economic growth and (b) the design of energy systems as 'technology incubators' for a prosperous 21st century.

The sustainability agenda is becoming an important element in business education. This is because there is a growing understanding of the need to develop strategic thinking that safeguards the long-term sustainability of business. To that end the Agent-based Computational Economics of the Global Energy System (ACEGES) project is introduced as a novel framework for developing a new generation of long-term decision scenarios. At a more general level, the project raises awareness of sustainable thinking in long-term business planning by means of controlled computational experiments. This paper describes the ACEGES tool, which has been introduced at a business school in the UK to educate students in the art and science of sustainable strategic thinking.