A novel numerical model for investigating macro factors influencing building energy consumption intensity

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Abstract

Building energy use accounts for 40% of the primary energy consumed worldwide. Therefore, it is essential to explore the key influencing factors and the associated interactions among these factors in order to achieve energy-saving goals. In practical scenarios, the sample size is always limited; thus, conventional models are unable to analyze the interactions among factors, or their results are unstable. To resolve this issue, this paper proposes a method that combines the improved stochastic impacts by regression on population, affluence, and technology (EM-STIRPAT) model and a structural equation modeling (SEM) model using the entropy weight method. The results obtained by analyzing the building energy use in Beijing, which was considered as a case study, indicate that economic level, technical level, industry structure, and education structure have a significant positive impact on building energy consumption intensity (BECI). Based on this analysis, it was found that 94% of the technical level and 39% of the economic level indirectly affect BECI through the energy structure. In addition, through a scenario analysis, it was found that future changes in the age structure of the population will have a smaller impact on BECI than changes in the education structure. Finally, based on the results of this study, policymaking recommendations to reduce building energy consumption are also presented.

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.

Reference (77)

  • Zhengxia He et al.

    Impact of urbanization on energy related co2 emission at different development levels: regional difference in china based on panel estimation

    J. Clean. Prod.

    (2017)
  • A. Heydarian et al.

    Immersive virtual environments, understanding the impact of design features and occupant choice upon lighting for building performance

    Build. Environ.

    (2015)
  • T. Hong

    A close look at the china design standard for energy efficiency of public buildings

    Energy Build.

    (2009)
  • S. Hu et al.

    A systematic review of occupant behavior in building energy policy

    Building and Environment

    (2020)
  • T. Huo et al.

    China's energy consumption in the building sector: a Statistical Yearbook-Energy Balance Sheet based splitting method

    J. Clean. Prod.

    (2018)
  • S. Kais et al.

    An econometric study of the impact of economic growth and energy use on carbon emissions: panel data evidence from fifty eight countries

    Renew. Sustain. Energy Rev.

    (2016)
  • C. Li

    How does environmental regulation affect different approaches of technical progress?—Evidence from china's industrial sectors from 2005 to 2015

    J. Clean. Prod.

    (2019)
  • Y. Lu et al.

    Which activities contribute most to building energy consumption in China? A hybrid LMDI decomposition analysis from year 2007 to 2015

    Energy Build.

    (2018)
  • M. Ma et al.

    Carbon abatement in China's commercial building sector: a bottom-up measurement model based on Kaya-LMDI methods

    Energy

    (2018)
  • V. Martinaitis et al.

    Importance of occupancy information when simulating energy demand of energy efficient house: a case study

    Energy .Build.

    (2015)
  • E.A. Mohareb et al.

    Decoupling of building energy use and climate

    Energy Build.

    (2011)
  • J. Ouyang et al.

    Energy-saving potential by improving occupants' behavior in urban residential sector in hangzhou city, china

    Energy .Build.

    (2009)
  • A. Tavakoli

    A journey among top ten emitter country, decomposition of “Kaya Identity”

    Sustain. Cities Soc.

    (2018)
  • D. Ürge-Vorsatz et al.

    Potentials and costs of carbon dioxide mitigation in the world's buildings

    Energy Policy

    (2008)
  • Q. Wang et al.

    Crude oil price: demand, supply, economic activity, economic policy uncertainty and wars–From the perspective of structural equation modelling (SEM)

    Energy

    (2017)
  • Z. Wang et al.

    Policy implications of the purchasing intentions towards energy-efficient appliances among china's urban residents: do subsidies work?

    Energy Policy

    (2017)
  • Y. Wei et al.

    Influential factors of national and regional CO2 emission in China based on combined model of DPSIR and PLS-SEM

    J. Clean. Prod.

    (2019)
  • S. Xiong et al.

    The impact of industrial structure efficiency on provincial industrial energy efficiency in china

    J. Clean. Prod.

    (2019)
  • S. Yang et al.

    Analyzing and optimizing the impact of economic restructuring on shanghai's carbon emissions using stirpat and nsga-ii

    Sustain. Cities Soc.

    (2018)
  • Y. Yang et al.

    Research on impacts of population-related factors on carbon emissions in beijing from 1984 to 2012

    Environ. Impact Assess. Rev.

    (2015)
  • J.C. Yeh et al.

    Impact of population and economic growth on carbon emissions in Taiwan using an analytic tool STIRPAT

    Sustain. Environ. Res.

    (2017)
  • Y. Zhang et al.

    Rethinking the role of occupant behavior in building energy performance: a review

    Energy .Build.

    (2018)
  • J. Zhao et al.

    A novel approach for urbanization level evaluation based on information entropy principle: a case of Beijing

    Phys. A.

    (2015)
  • J. Zhao et al.

    Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining

    Energy Build.

    (2014)
  • C. Zhou et al.

    Examining the determinants and the spatial nexus of city-level CO2 emissions in China: a dynamic spatial panel analysis of China's cities

    J. Clean. Prod.

    (2018)
  • H. Allcott et al.

    Energy. behavior and energy policy

    Science

    (2010)
  • R.P. Bagozzi et al.

    On the use of structural equation models in experimental designs

    J. Market. Res.

    (1989)
  • Beijing Statistical Bureau, Beijing statistical yearbook2019, in, 2017....
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