Temperature change and electricity consumption of the group living: A case study of college students
Graphical abstract
Note: The dependent variable in the panel (a) is the natural logarithm form of electricity consumption per capita. The line chart represents the estimated value of the effect of the average temperature on the dependent variable; the dashed line represents its 95% confidence interval; the value label represents each estimated coefficient value. The histogram in panel (b) represents the statistical histogram of the corresponding temperature group throughout the year, day as unit.
Introduction
There is a consensus that climate change has been a global challenge, and altering the human system through extreme climate events, changes in temperature and precipitation distribution, and deterioration of air quality (Montigny et al., 2011; IPCC, 2014; Burillo et al., 2019; Mares and Moffett, 2019). In recent years, the economics of climate change has become a major public issue. As IPCC points out that there will be a 2-degree increase in global surface average temperature by 2100, which would lead to more extreme heat and cold days. How to adapt the climate change sustainably with a reasonable global surface average temperature is drawing increasing attention. How to manage energy use in response to climate change is central to climate policy as its feedback effect. In an ever-warming world, one would expect more cooling demand, which could result in an increase in electricity consumption in the residential, industrial and commercial sectors. Cold countries will have benefits from the rise of temperature, as less energy will be used for heating during the winter months. Meanwhile, more energy will be required for cooling during hot summers in tropical countries (Li et al., 2019; Li et al., 2020; Zheng et al., 2020). China is facing a great diversity in electricity consumption across age and regions (Cheong et al., 2019).
The college student is a special group in the consumer. In many cases, college students represent a unique population of interest and may serve to provide insights that cannot be gleaned from other sub-populations in society (Robb, 2017). In China, there are about 30 million college students in 2019, which are approximately equal to the Australian population. The group of college students is aged from 19 to 28-year-old and replying on living expenses provided by parents. Due to the comparable ease budget, they have various consumption willing. Since most of them live in the shared dormitories with every 4 or 6 persons, their consumption patterns are sometimes irrational and susceptible to peer influence. How the special group would respond to climate change in terms of electricity consumption is significant in the policy implication in practical and academically.
Regarding the impact of temperature change on consumer behavior, Auffhammer and Mansur (2014) built a framework for analyzing consumer behavior based on environmental factors. There existed uncertainty in temperature effect on electricity consumption, where is mainly determined by some mediating factors. These mediating factors could be classified into two categories: personal (i.e. age, gender, and level of education) and consumption patterns including income levels, the share of electricity consumption expenditure, tariffs, type of electricity facilities, heating options (Laicāne et al., 2014). Temperature affects electricity consumption by demand of consumer to cooling and heating. When hot or cold days occurred, people who are more sensitive to changes in temperature will choose to use electricity to maintain their comfort (Zhou and Teng, 2013). At the same time individuals of higher educational level, the more likely it is to choose to use electricity (Chen et al., 2013; Santamouris and Kolokotsa, 2015). Auffhammer et al. (2017) found different temperature-electricity relationships in the west and east region of the USA. Zhang et al. (2019) pointed out a significant diversity in the impact of temperature on electricity consumption between the northern and southern regions in China. Due to limited available data at a meaningful scale, existing studies consistently conclude that hot weather significantly increases residential electricity consumption as the heterogeneity in the relationship is not identified. In comparison, the temperature-electricity relationship varies in regions. To our best of knowledge, Li et al. (2019) analyzed the impact of daily temperature on household electricity consumption at a household level and constructed a response relationship between temperature and electricity consumption. However, they all focused on the all-age population while there is little known about temperature-electricity consumption patterns for the special age-specific group.
Several studies began to explore the role of housing structure and building type on electricity consumption, such as housing floor height (Antonopoulos et al., 2019) and the orientation of house windows (Florides et al., 2002). Due to surface heat reflection, temperature varies with the height above the ground. In winter and summer, there is a difference between the surface air temperature and the upper air temperature (10 m above level). And as the floor level rises, the heat reflected from the surface decreases. The orientation of the windows also affects the heat balance of the house in winter and summer. For instance, in northern China, there will be a temperature difference in west-facing houses. In the summer mornings, north- and east-facing houses will have less sunlight, which leads to less increase in indoor temperature, while in the afternoons, the sunlight will enter the house with south and west-facing houses, therefore elevating indoor temperatures and increase demand for cool in summer. Jones and Lomas (2015) analyzed the factors that determined electricity demand in the case of UK households and found no correlation between floor and electricity demand. Chong (2012) found a significant negative correlation between electricity demand and the age of construction of the dwelling (vintage). It suggested the older house would use less electricity. However, there is not clear about the role of housing characteristics on electricity consumption behavior as climate patterns and architectural structure vary in regions. The underlying impact on residential behaviors in energy consumption is unknown at the individual level.
To fill the gap, we investigate the temperature-electricity relationship at a micro level in a representative climate pattern. Besides, we try to estimate the role of dwelling conditions in the temperature-electricity relationship. It can provide more evidence about how the human system response to climate change in a micro view. Further, the power sector can obtain more implications to ensure grid stability in an ever-warming climate. We use detailed microdata to investigate the temperature-electricity relationship combining with the dwelling information empirically. Our work provides evidence about how this relationship behaves in developing countries of specified climate patterns.
Section snippets
Data
We collect the monthly electricity consumption data of student dormitories from a college in Beijing, China from September 2018 to August 2019 and randomly select 50% of students' dormitories in the university. The dormitories are equipped with solo electricity meters that we could access the electricity consumption by month. So, we obtain the electricity consumption data by dormitory and month which covered 1343 dormitories. The number of the samples by gender, residential floor, windows
Benchmark results
Fig. 3 shows the relationship between average temperature and electricity consumption per capita. According to Eq. (1), with precipitation, air quality, and other variables controlled, it is found that the relationship between temperature and electricity consumption is approximately U-shaped, and the overall shape is consistent with Li et al. (2019). It can be seen from Fig. 2 that the impact of high temperature is greater than that of low-temperature weather. Compared with the benchmark
Discussion
We estimate the response of electricity consumption to temperature based on individual observations. Our sample comes from northern China, which is different from others in terms of climate conditions and cooling/heating patterns. So, we select the studies on household electricity consumption for comparative analysis, which select the researches of Auffhammer (2018) and Li et al. (2019). The major proportion of residential electricity is that for cooling and heating which the contributions are
Conclusion and implication
Based on samples of college students in Beijing, we investigate the impact of temperature change on residential electricity consumption. We find a U-shaped curve of temperature–electricity consumption relationship. The results show that the impact of high temperature is larger than that of low-temperature weather. Compared with the benchmark temperature group, every additional day of average temperature exceeding 30 °C will increase the monthly electricity consumption per capita by 16.8%, which
CRediT authorship contribution statement
XQL, CZ and HL designed the study. XQL, CZ and HL analyzed the data. XQL, CZ and YZ drafted the manuscript. XQL, CZ, YZ and HL contributed to the revision of the manuscript. All authors contributed to the interpretation of the results and approved the final version.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by National Natural Science Foundation of China (71521002, 71925008) and the International Graduate Exchange Program of Beijing Institute of Technology. We thank the comments from anonymous reviewers. The views expressed are solely the authors' own and do not necessarily reflect the views of the supporting agencies or the authors' affiliations.
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Chen Zhang is currently a Ph.D. candidate of the Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology (BIT), 5 South Zhongguancun, Haidian District, Beijing 100083, China.