A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China

Abstract

With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper is based on the industrial background of the energy consumption structure of Henan Province, a central province in the developing country of China. Firstly, the energy consumption intensity of buildings and public buildings in Henan Province from 2010 to 2020 was calculated according to the energy balance sheet. The Kaya–LMDI decomposition method was also used to analyse the carbon emissions of public buildings, determining the impact of each influencing parameter on public buildings. Secondly, the scenario prediction model Monte Carlo was run 100,000 times to set the stochastic parameters of the variables in the model to predict the time of carbon peak and carbon emissions. The analysis results indicated that: ① Carbon emissions in Henan Province have exhibited a steady growth trend, increasing from 1533 t in 2010 to 6561 t in 2020; ② The primary factors influencing carbon emissions of public buildings in Henan Province were urbanization rate, public floor area per capita, and energy intensity per unit of public floor area; and ③ Carbon emissions of public buildings in Henan Province followed an inverted U-shaped distribution and are expected to peak at approximately 7423 t by the year 2035. The research method in this paper can guide the simulation study of peak carbon emission prediction in Henan Province based on the influencing parameters of carbon emission from different types of buildings. Moreover, the results of this paper can provide a reference for a more precise study of building carbon reduction in similar regions of developing countries.

Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China

“…where C r denotes the total carbon emissions from rural residential buildings, E r denotes the total energy consumption of rural residential buildings, F r denotes the total floor area of rural residential buildings, e r denotes the energy consumption per unit area of rural residential buildings, f r denotes the floor area per capita of rural residential buildings, K r indicates the comprehensive carbon emission factor of rural residential buildings, and U r denotes the percentage of the rural population. Thus, the Kaya identity for the total carbon emissions from residential buildings in Henan Province can be written as Equation (17).…”

“…The LMDI decomposition method is highly practical and easily implemented, with no residual values remaining after the decomposition process to ensure unique results. Additionally, it guarantees homology across different operational decomposition methods [17][18][19]. After comparing and analyzing various index decomposition methods, Ang B W concludes that the Kaya-LMDI model, which characterizes carbon emissions based on the Kaya identity, is an excellent method for decomposing and analyzing factors influencing carbon emissions [20].…”

Decomposition of carbon emission factors in china's power industry: considering transmission and distribution

“…In terms of models, most scholars take economy or income, population, energy consumption intensity, fuel structure, carbon emission intensity, energy structure and industrial structure as factors influencing the change of carbon emissions in the power industry [14], [15], [16], [17], [18]. Most scholars have generally proved that socioeconomic factors such as economic development and population growth are the main driving factors of carbon emissions in the power industry, while energy factors such as energy structure can inhibit carbon emissions [19,20]. For example, Li et al [19] show that the main factors affecting carbon emissions in public buildings in Henan Province were identified as the urbanization rate, public floor area per capita, and energy intensity per unit of public floor area.…”

“…Most scholars have generally proved that socioeconomic factors such as economic development and population growth are the main driving factors of carbon emissions in the power industry, while energy factors such as energy structure can inhibit carbon emissions [19,20]. For example, Li et al [19] show that the main factors affecting carbon emissions in public buildings in Henan Province were identified as the urbanization rate, public floor area per capita, and energy intensity per unit of public floor area. Jiang et al [20] applied the electricity elasticity of carbon emissions to a decoupling index.…”