How does income level impact residential-building heating energy consumption? Micro-level evidence from household surveys
Introduction
The building sector is an important contributor to energy consumption and CO2 emissions (Huo et al., 2021b), and urban residential building energy consumption (URBEC) accounts for the largest proportion of building energy consumption, at nearly 40% (Huo et al., 2021a). The building sector embraces with higher energy-savings and emissions-reduction potential, which is as high as 74%, 1.5 times that of the industrial sector (McNeil, 2016; Zhou et al., 2018). With the improvement in China's urbanization level, URBEC will continue to increase. Therefore, the effectiveness of energy conservation and emissions reduction in the urban building sector is critical to China's carbon mitigation commitment and global climate response.
Heating is an important component of URBEC. Residential building heating energy consumption (BHEC) in the hot summer and cold winter (HSCW) zone of China has drawn special attention from industry and academia these days (Guo et al., 2018). The “Qinling-Huaihe” Line, as the geographical boundary between northern and southern China, was defined as the north-south heating line in the last century. The regional span in the HSCW zone is large and the variance in economic development between cities in this area is also significant. There has been a general call to change the status of indoor heating in winter, especially increasing demand for winter heating. In response to the public's call for heating demand, whether centralized heating should be implemented in the HSCW zone has become an urgent issue that must be addressed and has been debated for many years. If central heating is implemented in this zone, it might inhibit the development of energy conservation due to the large-scale construction, long-lasting cycle and huge investment in the centralized heating infrastructure. From the perspective of households with different income levels, it is difficult to realize efficiency and fairness if central heating is implemented in the HSCW zone. In terms of heating efficiency, residents have mainly adopted residential decentralized heating in the HSCW zone, which is easy to use. And households of different income levels can achieve corresponding heating needs at lower costs. From a fairness point of view, winter heating electricity use is unnecessary electricity use in such zone. If “one size fits all” centralized heating is implemented, it will squeeze the consumption space of the middle and low-income groups because “nonessential consumption” would transform into “essential consumption”. Therefore, the difference in household income will have an impact on residents' heating habits in the HSCW zone, which in turn will affect the government's macro-decisions and social equity and efficiency. Although previous scholars have studied heating issues from a relatively macro-perspective (Liu et al., 2020a; Liu et al., 2020b; Shao et al., 2018; Wang et al., 2021), the influence mechanism of household income on BHEC has remained unclear to date, especially at the micro-level. To bridge the above knowledge gap, this study attempts to examine the influencing factors affecting BHEC and to explore the impact mechanism of household income on BHEC. This study could boost the related theory system of BHEC and provide a strong reference for the government in implementing more effective heating and energy-saving policies.
The main contributions of this study include the three following aspects. First, we develop a splitting model-based calculation method for BHEC and apply it to BHEC in the HSCW zone of China. We consider a typical city, Chongqing, in China's HSCW zone, as a case and analyze the heating use characteristics. This process provides a reliable empirical basis for exploring the impact of income on BHEC and can also provide a methodological reference for the accounting of BHEC in other regions. Second, we establish a theoretical model of the impact of income on BHEC from the three perspectives of building physical characteristics, building use characteristics and energy-consuming equipment characteristics, which could enrich the theoretical system of the impact mechanism of building energy consumption. Third, we adopt a hierarchical regression method based on the sample data of our micro-survey during 2016–2018 to examine the impact of income on BHEC, to further explore the intermediate bridge between income and BHEC and to offer targeted suggestions.
The structure of the rest of this study is as follows: Section 2 provides a literature review in the related fields; Section 3 presents the theoretical models; Section 4 presents the research methods and survey data; Section 5 displays the empirical results and discusses them; and finally, conclusions and policy recommendations are provided in Section 6.
Section snippets
Literature review
Identifying the factors influencing energy consumption is significant for investigating the influence mechanism and energy consumption analysis (Liu et al., 2015; Mi et al., 2017; Miao et al., 2021; Shao et al., 2016). Scholars have commonly studied the factors influencing building energy consumption at the macro-level and micro-level. Macro-level studies have mainly focused on socioeconomic factors (Feng et al., 2018; Wang and Lin, 2017, Wang and Lin, 2018; Wang and Feng, 2018), such as GDP,
Theoretical model and research hypothesis
Centralized heating is a unique form of heating in northern China but not in the HSCW zone. Local decentralized heating is the major heating method in the HSCW zone, such as air conditioners and other heating appliances. Chongqing is a typical city in the HSCW zone, and the summer in this region is very hot, while the winter is cold and humid. The average temperature of the hottest month is between 27 °C and 31 °C and that in the coldest month is between 0 °C and 7 °C. When residents feel cold
Empirical method of the intermediary effect
The intermediary effect model is used to analyze the process and mechanism of the influence of the independent variables on the dependent variables (Liu, Liang, 2015). Intermediate analysis can yield more in-depth results than simply analyzing the effects of independent variables on dependent variables. The intermediary effect model is considered in the influence of the independent variable X on the dependent variable Y. If X affects Y by affecting the variable M, then M is called the
Characteristics of residential building use and building physics
Fig. 4a shows the distribution of the household income levels. Among the surveyed households, low-income families (less than 60,000 RMB yuan (approximately $8649)) accounted for 41%, middle-income families (60,000 yuan to 200,000 yuan) represented 54%, and high-income families (more than 200,000 yuan) accounted for 5%. Households with an annual income between 3000 yuan and 6000 yuan accounted for a large proportion.
Fig. 4b shows BHEC at different income levels. It shows that middle-income
Influencing factors of residential BHEC
According to the results, five factors are identified as the key factors affecting BHECs, including household income, residential building floor space, the amount of heating equipment, building energy efficiency standards, and the heating method. In contrast, significant impact relationships do not exist between residential BHEC and the remaining factors, including the floor, number of permanent residents, and length of residence in the buildings.
In general, the results of the study are
Conclusions and policy implications
This study developed a splitting model-based calculation method of BHEC and analyzed the characteristics of BHEC in the HSCW zone by applying this model. Then, we examined the influencing factors affecting BHEC and explored the impact mechanism of household income on BHEC. The main findings are as follows. (1) Household income, residential building floor space, the amount of heating equipment, building energy efficiency standards, and the heating method have significant impacts on BHEC, and
CRediT authorship contribution statement
Tengfei Huo: Conceptualization, Methodology, Writing – original draft. Weiguang Cai: Supervision, Investigation. Weishi Zhang: Visualization, Data curation and Revising. Jing Wang: Writing – original draft, Investigation, Validation. Ya Zhao: Visualization, Investigation, Software, Validation. Xisheng Zhu: Supervision.
Declaration of Competing Interest
All co-authors agree with the contents of the manuscript and there is no financial interest to report.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 71902053 and 71722004), Natural Science Foundation of Hebei Province (Grant No. G2021202003) and Fundamental Research Funds for the Central Universities (No. 2021CDJSKCG27).
References (49)
- et al.
Do investment and improvement demand outweigh basic consumption demand in housing market? Evidence from small cities in Jiangsu, China
(2017) - et al.
The driving forces and potential mitigation of energy-related CO2 emissions in China’s metal industry
Res. Policy
(2018) - et al.
North-south debate on district heating: evidence from a household survey
Energy Policy
(2015) - et al.
Advances in research and applications of energy-related occupant behavior in buildings
Ener. Build.
(2016) - et al.
Decoupling and decomposition analysis of residential building carbon emissions from residential income: Evidence from the provincial level in China
Environ. Impact. Assess. Rev.
(2021) - et al.
China’s energy consumption in the building sector: a statistical yearbook-energy balance sheet based splitting method
J. Clean. Prod.
(2018) - et al.
China’s building stock estimation and energy intensity analysis
J. Clean. Prod.
(2019) China’s urban residential carbon emission and energy efficiency policy
Energy.
(2016)- et al.
Quantifying the potential impacts of China’s power-sector policies on coal input and CO2 emissions through 2050: a bottom-up perspective
Util. Policy
(2016) - et al.
Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR
Remote Sens. Environ.
(2016)
CO2 emissions of China’s commercial and residential buildings: evidence and reduction policy
Build. Environ.
Accounting for China’s regional carbon emissions in 2002 and 2007: production-based versus consumption-based principles
J. Clean. Prod.
Exploring the critical factors and appropriate polices for reducing energy consumption of China’s urban civil building sector
J. Clean. Prod.
Analysis of typical public building energy consumption in northern China
Energ. Build.
Feng W, du can SdlR, Khanna NZ, Ke J, Zhou N. energy efficiency outlook in China’s urban buildings sector through 2030
Energy Policy
Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition
Appl. Energ.
The effect of building envelope on the thermal comfort and energy saving for high-rise buildings in hot–humid climate
Renew. Sust. Energ. Rev.
Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes: a case study for Shanghai (China)
Renew. Sust. Energ. Rev.
Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China
World Dev.
An empirical case study about the reform of tiered pricing for household electricity in China
Appl. Energy
Price and expenditure elasticities of residential energy demand during urbanization: an empirical analysis based on the household-level survey data in China
Energy Policy
Exploring the driving forces of energy-related CO2 emissions in China’s construction industry by utilizing production-theoretical decomposition analysis
J. Clean. Prod.
Assessing CO2 emissions in China’s commercial sector: determinants and reduction strategies
J. Clean. Prod.
Dynamic change in energy and CO2 performance of China’s commercial sector: a regional comparative study
Energy Policy
Cited by (31)
Subsidy allocation for residential building energy retrofit: A perspective of families' incomes
2024, Sustainable Cities and SocietyInvestigating CO<inf>2</inf> emissions and disparity from China's central heating: A perspective at the city level
2023, Environmental Impact Assessment ReviewData-driven analysis of influential factors on residential energy end-use in the US
2023, Journal of Building EngineeringAssessment of determinants for households' pro-environmental behaviours and direct emissions
2023, Journal of Cleaner Production