期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《工程管理前沿(英文)》 >> 2017年 第4卷 第2期 doi: 10.15302/J-FEM-2017029

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

The School of Architecture, Design, and The Built Environment, Nottingham Trent University, Nottingham, NG1 4BU, UK

录用日期: 2017-07-05 发布日期: 2017-07-17

下一篇 上一篇

摘要

The household sector consumes roughly 30% of Earth’s energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO emissions, which are associated with the world’s rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption. Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures (e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model “EvoEnergy” to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making.

相关研究