A novel numerical model for investigating macro factors influencing building energy consumption intensity
Introduction
The energy consumed in buildings accounts for 40% of the primary energy used worldwide (Ürge-Vorsatz and Novikova, 2008). However, building energy use is substantially affected by several factors such as weather conditions and building envelopes. Therefore, to achieve energy-saving goals, it is crucial to explore the key factors and their associated impacts that influence building energy use. In recent years, the interactions among such influencing factors of building energy consumption and carbon emissions have attracted increasing research attention worldwide (Wei et al., 2019). This is because an incomplete understanding of these interactions may lead policy makers to draw incorrect conclusions, which can be detrimental to resolving practical problems such as energy saving and emission reduction.
Structural equation modeling (SEM) is typically used to elucidate the mutual interactions among factors; however, its results are affected by the sample size, especially when solving complex models (Bentler and Bonett, 1980; Bagozzi and Yi, 1989). Moreover, as the number of samples in practical problems is limited, using this method can yield unstable results.
To address these problems, this study uses information entropy to combine the improved stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, termed as the EM-STIRPAT model, and an SEM model. The number of parameters introduced in the SEM model is reduced via the entropy weight method, which is based on the results of the EM-STIRPAT model. As a result, the combined method does not affect the relationships among different factors and also improves the calculation speed and convergence probability of the SEM model.
To verify the effectiveness and reproducibility of the model, Beijing was considered as case study, and the proposed combined model was employed to explore the impact of macro factors on the building energy consumption intensity (BECI) in Beijing.
It should be noted that, in this study, building energy consumption refers to the energy consumed during the construction and operation phases of buildings, including public and residential buildings. From the perspective of the entire life cycle of a building, energy is consumed during various periods such as the production and transportation of building materials, construction of the building, operation phase, demolition of the building, and recycling of materials after demolition. From a narrower perspective, building energy only includes the energy consumed during the construction and operation phases of a building. Therefore, the energy required for producing and recycling materials can be ascribed to the energy consumed during related industrial and manufacturing processes, and the energy consumed during transportation can be attributed to the transportation industry. This is a result-oriented division, which does not consider the purpose of material production and transportation. In addition, the Chinese statistical department uses the equivalent standard coal as the energy unit; the same unit is also considered in this study. This work investigates the total energy consumption of all the buildings, including residential and public buildings, in Beijing.
Section snippets
Methods for analyzing influencing factors of building energy consumption intensity
Several methods have been proposed to determine and clarify the influencing factors of BECI; these methods mainly include the input–output method (Ascione et al., 2016; Guo et al., 2018), logarithmic mean divisor index (LMDI) model (Garza-Gil et al., 2017; Ma et al., 2018; Fang et al., 2019; Achour and Belloumi, 2016; Olanrewaju, 2019), and STIRPAT model (Yeh and Liao, 2017). The input–output method is used to study the interdependence among the inputs and outputs for various sectors of the
Framework of combined models
The structure of the combined model is presented in Fig. 1. SEM models (e.g., PLS-SEM) can adequately solve the interactions between factors; however, their results are strongly dependent on the sample size, especially when dealing with complex interaction relationship issues (Bentler and Bonett, 1980; Bagozzi and Yi, 1989; Wold, 1982; Fornell and Bookstein, 1982). This implies that the path structure (including the complexity and number of paths) of the factors is crucial for solving the SEM
Results of EM-STIRPAT model
Considering Beijing as a case study, data from 2004 to 2011 are used to solve the EM-STIRPAT model. Moreover, additional data from five other years are used to verify the validity and accuracy of the model. The solution of factor coefficients is necessary to calculate the information entropy and weight coefficient presented in Section 3.1.5. Subsequently, the weight coefficient is used to simplify the path input to the SEM model. Fig. 4 presents Beijing's BECI0 (corresponding to I0 in Eq. (3))
Conclusions and policy implications
This paper proposed a method combining the EM-STIRPAT model and the SEM model. The proposed method was verified considering Beijing City as a cases study. The main findings are as follows:
- (1)
The combined EM-STIRPAT and SEM model can analyze the interactions among factors and also predict building energy consumption owing to its statistical regression characteristics. Moreover, the EM-STIRPAT model is more robust than the STIRPAT model.
- (2)
The entropy weight method is adopted for reducing the number of
Declaration of competing interest
This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. All study participants provided informed consent, and the study design was approved by the appropriate ethics review board. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.
Acknowledgments
This research work was supported by the National Key R&D Program of China (No. 2018YFC0704400) and the Fundamental Research Funds for the Central Universities of China under Grant (No.2020CDJQY-A009). The authors would like to thank Ms. Shangyan Wu from Chongqing University for her valuable suggestions, support and company.
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