• Energy Research
• Restricted close
• 11. Sustainability close
• Apollo close

Electricity is an undisputed factor supporting human development, while further supporting social wellbeing and fostering economic growth of modern societies. Therefore, the electricity market provides a vivid policy-making arena for the EU regulators, where on-going structural reforms are promoted with the aim to encapsulate and accommodate sustainability aspects. Notably, the EU Member States have adopted the strategic roadmap “Europe 2020” toward reducing greenhouse gas emissions and energy consumption by 20 %, and generating 20 % of energy from renewable sources. However, our research highlights that strategies trying to balance sustainable electricity supply with demand often neglect the societal pricing acceptability of the electricity provided to consumers. Thus far, existing literature focuses on electricity pricing policies that fail to capture the dynamics that have to govern realistic pricing schema in the electricity sector. To that end, this study elaborates the system dynamics (SD) methodological approach to embrace the potential dominant factors within a sustainable electricity system. The proposed SD framework could assist public and private stakeholders in determining a rational electricity pricing policy within a sustainability context. Finally, policy-making interventions are discussed in order to provide managerial insights for the decision-makers.

Electricity is an undisputed factor supporting human development, while further supporting social wellbeing and fostering economic growth of modern societies. Therefore, the electricity market provides a vivid policy-making arena for the EU regulators, where on-going structural reforms are promoted with the aim to encapsulate and accommodate sustainability aspects. Notably, the EU Member States have adopted the strategic roadmap “Europe 2020” toward reducing greenhouse gas emissions and energy consumption by 20 %, and generating 20 % of energy from renewable sources. However, our research highlights that strategies trying to balance sustainable electricity supply with demand often neglect the societal pricing acceptability of the electricity provided to consumers. Thus far, existing literature focuses on electricity pricing policies that fail to capture the dynamics that have to govern realistic pricing schema in the electricity sector. To that end, this study elaborates the system dynamics (SD) methodological approach to embrace the potential dominant factors within a sustainable electricity system. The proposed SD framework could assist public and private stakeholders in determining a rational electricity pricing policy within a sustainability context. Finally, policy-making interventions are discussed in order to provide managerial insights for the decision-makers.

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).