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
|更新时间:2026-05-27
|
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
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.
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.
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
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
Keywords
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.
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.
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.
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.