The Quantification of Carbon Emission Factors for Residential Buildings in Yunnan Province

The carbon emissions released from buildings are correlated with various factors in social and economic systems. Thus, quantifying and then controlling those factors can decrease the release of carbon emissions further. To quantify the influencing factors of the carbon emissions of residential build...

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Veröffentlicht in:Buildings (Basel) 2024-04, Vol.14 (4), p.880
Hauptverfasser: Li, Wuyan, Li, Qinyao, Zhang, Chubei, Jin, Sike, Wang, Zhihao, Huang, Sheng, Deng, Shihan
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Sprache:eng
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Zusammenfassung:The carbon emissions released from buildings are correlated with various factors in social and economic systems. Thus, quantifying and then controlling those factors can decrease the release of carbon emissions further. To quantify the influencing factors of the carbon emissions of residential buildings in Yunnan Province in China, separately for urban and rural areas, this study adopted the methods of utilizing the carbon emission factor and the LMDI model and combined them with the carbon emissions data obtained from 2010 to 2019. Subsequently, with this model, the contribution of each factor to the overall carbon emissions was quantified. The results demonstrate the following: (1) the main factors influencing carbon emissions from residential buildings include the per capita floor area, energy consumption per unit area, energy intensity effect, energy structure effect, urbanization rate, and population size. (2) For urban buildings, carbon emissions are negatively correlated with the energy consumption per unit area, energy intensity effect, and energy structure effect, with contribution values of 0.34, 0.27, and 0.05, respectively. Conversely, there is a positive correlation with the per capita floor area, urbanization rate, and population size, with contribution values of 0.23, 0.11, and 0.01, respectively. (3) For rural buildings, carbon emissions are negatively correlated with urbanization rate, energy intensity effect, and energy structure effect, with contribution values of 0.16, 0.15, and 0.14, respectively. Conversely, there is a positive correlation with the per capita floor area, energy consumption per unit area, and population size, with contribution values of 0.29, 0.24, and 0.02, respectively.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14040880