首页|Fine-scale estimation of building operation carbon emissions: A case study of the Pearl River Delta Urban Agglomeration

Fine-scale estimation of building operation carbon emissions: A case study of the Pearl River Delta Urban Agglomeration

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  • NETL
  • NSTL
  • Tsinghua Univ Press
Building operations are a significant source of urban carbon dioxide (CO_2) emissions. However, the specific amounts and spatiotemporal distribution of these emissions remain unclear, complicating targeted emission reduction goals. This study introduces a building-level CO_2 emissions estimation method and applies it to the Pearl River Delta Urban Agglomeration (PRDUA). By integrating the Designer's Simulation Toolkit (DeST) for electricity consumption modeling with an energy decomposition approach for natural gas (NG) and liquefied petroleum gas (LPG) usage, we calculated CO_2 emissions for each building using specific carbon emission factors. The methodology was validated in terms of the electricity consumption intensity per square meter and the monthly electricity consumption of individual buildings. In 2021, the annual hourly emission peak in the PRDUA was 26.1 thousand tons, with a low of 606.2 t. Commercial buildings have the highest monthly CO_2 emission intensity per unit area (MCEIA) among all building types, ranging from 3.7 kgCO_2/(m~2·mo) in February to 6.9 kgCO_2/(m~2·mo) in July. The total annual CO_2 emissions from buildings in the PRDUA were 82.14 million tons, with the top four cities accounting for 75.6% of the emissions; the remaining five cities contributed only 24.4%, highlighting a significant imbalance. Residential and commercial buildings were responsible for 76% of total emissions, emphasizing the disparity in contributions among different building categories. By mapping the spatiotemporal distribution of emissions, we identified the critical areas for targeted carbon reduction. The proposed method provides a robust framework for supporting sustainable urban energy management and guiding effective carbon mitigation strategies.

building-level CO_2 emissionsDeSTbuilding typesspatiotemporal distribution

Geng Liu、Yue Zheng、Xiaocong Xu、Xiaoping Liu、Honghui Zhang、Jinpei Ou

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School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Vat-sen University, Guangzhou 510275, China

School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Vat-sen University, Guangzhou 510275, China||Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China||Guangdong Guodi Planning Science Technology Co., Ltd., Guangzhou 510650, China

2025

10.1007/s12273-025-1265-3

Building Simulation

Building Simulation

ISSN：1996-3599
年,卷(期)：2025.18(5)
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