地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (5): 1063-1072.doi: 10.12082/dqxxkx.2020.190727

• “空间综合人文学与社会科学”专辑 • 上一篇    下一篇

武汉居民建筑物碳排放反演计算和时空分析

贾涛1, 杨仕浩1, 李欣2,*(), 鄢鹏高1, 喻雪松1, 罗希1, 陈凯1   

  1. 1.武汉大学遥感信息工程学院,武汉430072
    2.厦门大学建筑与土木工程学院,厦门361005
  • 收稿日期:2019-11-29 修回日期:2020-03-19 出版日期:2020-05-25 发布日期:2020-07-25
  • 通讯作者: 李欣 E-mail:li-xin@whu.edu.cn
  • 作者简介:贾 涛(1983— ),男,山西运城人,副教授,主要从事地理信息科学、时空大数据分析与挖掘研究、人类动力学、复杂网络等研究。E-mail: tao.jia@whu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41971332)

Computation of Carbon Emissions of Residential Buildings in Wuhan and Its Spatiotemporal Analysis

JIA Tao1, YANG Shihao1, LI Xin2,*(), YAN Penggao1, YU Xuesong1, LUO Xi1, CHEN Kai1   

  1. 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
    2. School of architecture and civil engineering, Xiamen University, Xiamen 361005, China
  • Received:2019-11-29 Revised:2020-03-19 Online:2020-05-25 Published:2020-07-25
  • Contact: LI Xin E-mail:li-xin@whu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41971332)

摘要:

居民建筑物(民用住宅建筑物)碳排放对节能减排策略制定及城市可持续发展具有重要影响。针对目前城市碳排放计算方法尺度较大且缺乏居民建筑物碳排放一致性计算的问题,本文提出一种多源数据融合的城市居民建筑物碳排放定量计算方法。该方法首先采用自上而下的策略,结合夜间灯光图像,将武汉市居民碳排放总量分配到每个地块;然后采用自下而上的策略,构建由地块规划因子、社会经济因子以及单体居民建筑物形态因子组成的居民建筑物碳排放反演模型。论文使用该方法计算了武汉市所有单体居民建筑物的电能消耗碳排放量,研究结果表明:① 居民建筑物碳排放量在空间分布上呈现由中心城区向郊区不断递减的模式,和人口分布有着密切的关系;② 居民建筑物碳排放量分布具有异质性,呈现出长尾分布的特性,其中89%的居民建筑物的碳排放量低于平均值1.28 t,而11%的居民建筑物的碳排放量高于平均值;③ 同一地块上的居民建筑物碳排放量差异相对较小,地块之内平均标准差为7.66 t,而不同地块上的居民建筑物碳排放量差异相对较大,地块之间平均标准差达到51.30 t;④ 居民建筑物的碳排放量更容易受到规划因子中的容积率影响,社会经济因子中的人口密度影响,以及居民建筑物形态类型的影响。论文研究方法及相关研究成果可以为城市居住区可持续规划等问题提供决策支持。

关键词: 居民建筑物, 碳排放, 夜间灯光, 时空分析, 城市地块, 规划因子, 社会经济因子, 武汉

Abstract:

Carbon emissions of residential buildings have an important impact on energy conservation policies, emission reduction strategies, and sustainable urban development. However, current studies mainly focus on carbon emission estimation for an entire city or a large region. There is a lack of consistent methods of carbon emission estimation for residential buildings. Thus, this paper proposes a method to calculate carbon emissions of residential buildings by the fusion of multiple datasets. Our method firstly uses a top-down based strategy to assign the total carbon emission to each urban block. Then it adopts a bottom-up strategy to establish an emission calculation model for each residential building by taking into account urban block planning factors, socioeconomic factors, and residential building morphological factors. This paper applies the proposed method to estimate carbon emissions of all residential buildings in Wuhan city. Our results show that: (1) Carbon emissions of residential buildings decreases from the central city to the suburbs, which is closely related to population distribution; (2) Carbon emissions of residential buildings are heterogeneous and exhibit a heavy-tailed distribution. For instance, there are 89% of residential buildings with carbon emission lower than the average of 1.28 ton and 11% of residential buildings with carbon emission higher than the average; (3) Residential buildings within the same urban block have slight difference in carbon emission with an average standard deviation of 7.66 ton, while residential buildings located in different urban blocks tend to have significantly different carbon emissions with an average standard deviation of 51.30 ton; and (4) Carbon emissions of residential buildings are more likely to be affected by plot ratios in planning factors, population density in socioeconomic factors, and shapes of residential buildings. Our method and experimental results can provide decision support for sustainable planning of urban residential areas.

Key words: residential building, carbon emission, nighttime light, spatiotemporal analysis, urban block, planning factor, socioeconomic factor, Wuhan