城市居民生活能源消费研究进展综述

近十年来中国城市消费领域能耗年增长率已达到7.4%,超过中国总能耗量5.9%的年增长率。随着中国逐渐完成工业化进程,其工业用能将呈现增速放缓甚至总量减少的趋势,而居民生活及交通能源需求将随着人民生活水平提高而稳步增长,最终呈现总量与比重逐渐增加的态势。城市居民活动与其能源消费的定量化分析已成为当前多学科的研究热点。本文以家庭室内和室外直接能源消费作为研究对象,对城市居民生活能耗的影响因素、能源足迹核算方法、时间-室内能耗模拟,及空间-交通-能源耦合模拟等关键问题的研究进展进行综述。研究发现,第一,家庭能源消费属于派生性需求,不同的时间利用方式会产生能源消费水平和结构上的差异,但现有研究较少从时间及活动链分析角度展开。第二,由于家庭预算及时间约束的存在,室内外能源消费行为密切相关,但少有研究对上述两个城市生活部门的能源消费进行整体性分析核算。第三,基于活动的建模方法可以提供一种将居民室内外用能行为进行整合的分析框架。最后对今后该领域的研究开展进行了展望:第一,从活动分析和时间利用视角,建立自下而上的居民室内外用能活动仿真模型,在更小的时间和空间尺度模拟家庭能源需求,识别家庭能耗的主要来源、控制的重点人群、时段和区域,指导能源政策制定;第二,深入剖析能源回弹效应产生的决策机理,以及怎样的政策或政策组合可以有效减少回弹;第三,将社会网络分析引入居民生活能耗研究,更加全面理解影响家庭用能行为的机制,为家庭能源消费管理提供新思路。

Review of studies on energy consumption of urban households

Over the last decade, the growth rate of urban China's domestic energy consumption has reached 7.4%, more than that of China's total energy consumption, which is 5.9%.It is believed that, with industrial reconstruction and revolution, household energy demand will represent an even more influential proportion of China's total energy consumption.Urban household energy consumption has attracted increasing attention in recent years.This article, focusing on household direct indoor and outdoor energy consumption, reviews most recent progresses in this field, including identification of influential factors, accounting domestic energy footprints, real-time domestic energy modelling and integrated land use-transport-energy modelling.An examination of current practices reveals that, firstly, household energy consumption represents derived demand.Different ways of allocating time can result in varieties of the amount and structure of energy consumption.However, little relevant literature to date has examined from the perspective of activity schedule and time allocation.Secondly, due to the existence of financial budget and time constraint, indoor and outdoor energy consuming activities are highly correlated.However, little has been done in the integrated analysis of these two domestic sectors.Thirdly, this article highlights the feasibility of using activity-based modelling as an integrated analysis framework.This article ends with three potential directions for future discussion: ①constructing a bottom-up activity-based domestic energy demand model for urban households, at a higher temporal and spatial resolution, in order to serve as a supplementary tool for governors to identify the sub-population, time periods and regions in need of special control;②examining the decision-making mechanism behind energy rebound effect, and preliminarily designing effective tools to reduce such effect;③integrating social network into the accounting model, in order to have a more thorough and accurate understanding of household energy consuming features.