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A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior: Three case studies in China

  • Research Article
  • Architecture and Human Behavior
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Abstract

Along with the improvement of social productivity and living standard, residential buildings generate a growing portion of carbon emissions, especially during the operation stage. However, energy use behaviors are usually ignored in carbon emission calculation. This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions. Firstly, we investigated energy use behaviors in dwellings across three cities in China: Xi’an, Shanghai and Fuzhou. Then, we established calibrated carbon emission models and optimization models with different green building measures for residential buildings. The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions. The carbon emissions of residential electricity bills in Xi’an, Shanghai and Fuzhou are 13.6 kgCO2/(m2·a) (excluding central heating), 29.3 kgCO2/(m2·a) and 17.2 kgCO2/(m2·a), respectively. Equipment carbon emissions account for 32.2%–64.1% of the total. In comparison to the model based on internal standard setting, the accuracy of the models using actual internal has improved by 25.9%–37.4%. The three-star green building methods have the highest carbon reduction rate among different star buildings, the emission reduction rates are around 30%. This study’s findings are useful for carbon emission calculation and green building design of residential buildings in the future.

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Abbreviations

A i :

floor area of the ith household (m2)

CEF:

carbon emission factor (kgCO2/kWh)

CEI:

carbon emission intensity (kgCO2/m2)

CER:

contribution emission reduction

ē :

average power density of the appliance (W/m2)

EC:

energy consumption (kWh)

ECI:

energy consumption intensity (kWh/m2)

E i,j :

power of the jth appliance owned by the ith household (W)

E i,t :

power of appliances being used in the ith household at the hth hour (W)

K e,h :

simultaneous use factor of appliance at the hth hour

K p,h :

simultaneous factor of occupancy

m :

total number of electrical equipment used in a household

m h :

number of electrical equipment in use in a household

MRE:

monthly average relative error

n :

total number of households

\(\bar P\) :

average number of households

P i :

total number of occupants in the ith household

P i,h :

number of occupants at the hth hour by the ith household

SHGC:

solar heat gain coefficient

U roof :

roof heat transfer coefficient (W/(m2·K))

U wall :

exterior wall heat transfer coefficient (W/(m2·K))

U win :

window heat transfer coefficient (W/(m2·K))

base:

baseline model

gm:

green model

h :

hour

i :

household number

j :

appliance number

r:

real situation

s:

simulation situation

t :

appliance number in use hourly

z :

month

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Acknowledgements

This work was funded by the Youth Program of the National Natural Science Foundation of China (No. 51908006) and supported by Beijing lemon tree green building technology co., LTD.

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Authors and Affiliations

  1. Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, 100124, China

    Menghan Niu, Ying Ji & Miao Zhao

  2. College of Architecture and Urban Planning, Tongji University, Shanghai, 201804, China

    Jiefan Gu

  3. The Bartlett School of Architecture, University College, London, WC1E 6BT, UK

    Aonan Li

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  1. Menghan Niu
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  2. Ying Ji
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  3. Miao Zhao
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  4. Jiefan Gu
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Correspondence to Ying Ji.

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Niu, M., Ji, Y., Zhao, M. et al. A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior: Three case studies in China. Build. Simul. 17, 147–164 (2024). https://doi.org/10.1007/s12273-023-1070-9

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