城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例

张瀚文; 梁泽锋; 刘琳; 刘丽孺

doi:10.12052/gdutxb.250051

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城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例

综合研究 | 更新时间：2026-05-27

- 城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例
- Time-varying Calculation Method and Application of Carbon Emissions at the Urban Community Scale—A Case Study of three High-density Residential Areas in Guangzhou
- 广东工业大学学报 2025年42卷第6期页码：134-144
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 作者简介：
  
  张瀚文(1999–)，男，硕士研究生，主要研究方向为湿热地区碳排放核算，E-mail: 245476490@qq.com
  刘琳(1991–)，女，副教授，博士，主要研究方向为城市热环境，E-mail: liulinhit@163.com
- 基金信息：
  
  国家自然科学基金资助项目(52208099);广州市科技计划项目(2025A04J5204)
- DOI：10.12052/gdutxb.250051
  中图分类号： X321;TU984
- 纸质出版：2025
- 稿件说明：
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张瀚文, 梁泽锋, 刘琳, 等. 城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例[J]. 广东工业大学学报, 2025, 42(6): 134–144.

Zhang Hanwen, Liang Zefeng, Liu Lin, et al. Time-varying Calculation Method and Application of Carbon Emissions at the Urban Community Scale—A Case Study of three High-density Residential Areas in Guangzhou[J]. 2025, 42(6): 134-144.
张瀚文, 梁泽锋, 刘琳, 等. 城市社区尺度碳排放时变计算方法及应用——以广州某三个高密度住区为例[J]. 广东工业大学学报, 2025, 42(6): 134–144. DOI： 10.12052/gdutxb.250051

Zhang Hanwen, Liang Zefeng, Liu Lin, et al. Time-varying Calculation Method and Application of Carbon Emissions at the Urban Community Scale—A Case Study of three High-density Residential Areas in Guangzhou[J]. 2025, 42(6): 134-144. DOI： 10.12052/gdutxb.250051.

摘要

为应对全球气候变化与城市低碳转型需求，本文聚焦社区尺度碳排放的时变特征与多要素耦合效应，提出一种精细化计算方法。以广州市3个典型高密度住区为研究对象，通过构建建筑能耗动态模拟模型、交通出行统计模型及市政设施核算框架，结合逐时气象数据与实地调研参数以及碳排放因子法，量化计算了建筑、交通、市政等层面多要素耦合的碳排放量。结果表明，3个社区的建筑碳排放时变特性明显，居住建筑和商业建筑的碳排放高于其他类型建筑，且居住建筑碳排放量在建筑碳排放量中占比最高；交通碳排放中由汽油作为碳排放源驱动的交通碳排放占比高达76%，私家车则在所有交通出行方式中的碳排放量最大；市政设施碳排放中污水处理部分占比用水总碳排放达到了63%，垃圾处理碳排放中焚烧和填埋的碳排放量要远高于堆肥；道路照明碳排放中支路照明占比超过50%。此外，单位附属绿地的固碳量是4类绿地中最少的。综上，本文通过对社区尺度多要素碳排放的核算，明确了社区内空间结构、人口密度和建筑类型对碳排放影响和社区不同类型碳排放的现状。

Abstract

To address the challenges of global climate change and the imperative of urban low-carbon transformation

this study focused on the temporal characteristics and multi-factor coupling effects of community scale carbon emissions. A refined calculation method was proposed and applied to three typical high-density residential areas in Guangzhou. Based on dynamic simulation models for building energy consumption

transportation statistics

and municipal facility accounting frameworks

this study quantifies carbon emissions across buildings

transportation

and municipal services by integrating field survey parameters and carbon emission factors. The results revealed notable temporal variation in building carbon emissions

the residential and commercial buildings displayed higher emissions than other building types

with residential buildings accounting for the largest share of total emissions. Transportation emissions were primarily driven by gasoline-powered vehicles

constituting 76% of total transportation emissions

with private cars being the largest contributor. The wastewater treatment contributed 63% of carbon emissions from water use and the composting had the lowest carbon emission in waste treatment. In urban road lighting carbon emissions

branch road lighting exceeds 50%. Also

the carbon sequestration capacity per unit area of green space is lowest among all types. This study clarifies the impacts of spatial structure

population density

and building types on carbon emissions by accounting the multi-factor carbon emissions in community scale.

关键词

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references

GU C L, TAN Z B, LIU W, et al . A study on climate change, carbon emissions and low-carbon city planning[J ] . Urban Planning Forum, 2009(3): 38-45.

HU A G. China's goal of achieving carbon peak by 2030 and its main approaches[J]. Journal of Beijing University of Technology(Social Sciences Edition), 2021, 21(3): 1-15.

ZHAN L P, ZHAO R, LIU S Y, et al . Spatial and temporal distribution characteristics of community carbon emissions based on inventory accounting method[J ] . Sichuan Environment, 2020, 39(3): 182-188.

LIN T, YU Y, BAI X, et al . Greenhouse gas emissions accounting of urban residential consumption: a household survey based approach [J ] . PLOS One, 2013, 8(2): e55642.

MA C, JU M T, ZHANG X C, et al . Energy consumption and carbon emissions in a coastal city in China[J ] . Procedia Environmental Sciences, 2011, 4: 1-9.

HUO T, MA Y, XU L, et al . Carbon emissions in China's urban residential building sector through 2060: a dynamic scenario simulation[J ] . Energy, 2022, 254: 124395.

吉静, 李玉展, 林瑜. 基于交通大数据的宏观碳排放计算与决策支持应用研究[C]// 第十届中国智能交通年会优秀论文集. 北京: 电子工业出版社, 2015: 377-386.

REN F, LONG D. Carbon emission forecasting and scenario analysis in Guangdong province based on optimized fast learning network[J]. Journal of Cleaner Production, 2021, 317: 128408.

YUAN L, KANG Y, WANG S, et al . Effects of thermal insulation characteristics on energy consumption of buildings with intermittently operated air-conditioning systems under real time varying climate conditions[J ] . Energy and Buildings, 2017, 155: 559-570.

彭惠旺. 珠三角地区商业和住宅建筑的典型建筑能耗模型研究[D]. 广州: 广州大学, 2020.

TIAN X, ZHANG H, LIU L, et al . Establishment of LCZ-based urban building energy consumption dataset in hot and humid subtropical regions through a bottom-up method[J ] . Applied Energy, 2024, 368: 123491.

阴世超. 建筑全生命周期碳排放核算分析[D]. 哈尔滨: 哈尔滨工业大学, 2012.

广东省住房和城乡建设厅. 建筑碳排放计算导则(试行) [S]. 广州: 广东省住房和城乡建设厅, 2021.

张铁映. 城市不同交通方式能源消耗比较研究[D]. 北京: 北京交通大学, 2010.

常超凡. 城市居民短距离出行行为研究[D]. 北京: 北京交通大学, 2007.

彭永伦. 电动汽车汽油车节能减排比较研究[J]. 北京汽车, 2017(5): 1-4.

SHI X Q, LI X N, YANG J X. Research on carbon reduction potential of electric vehicles for low-carbon transportation and its influencing factors[J]. Environmental Science, 2013, 34(1): 385-394.

GUO D, ZHANG H H, ZHENG C Y, et al . Analysis of the future development of Chinese auto energy saving and environmental benefits[J ] . Systems Engineering—Theory Practice, 2016, 36(6): 1593-1599.

广东省水利厅. 2020年广东省水资源公报[R]. 广州: 广东省水利厅, 2020.

中华人民共和国住房和城乡建设部, 室外排水设计标准: GB 50014—2021[S]. 北京: 中国建筑工业出版社, 2021.

余娇. 基于“水—能—碳”关联的郑州市水系统碳排放研究[D]. 郑州: 华北水利水电大学, 2020.

HUANG Z X. Exploration and practice of "Guangzhou Experience" in waste treatment[J]. Synthetic Materials Aging and Application, 2022, 51(1): 113-116.

ZHONG L, HU Y, WANG L. Calculation and reduction consideration of greenhouse gas emission of MSW[J]. Environmental Sanitation Engineering, 2019, 27(5): 45-48.

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