Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model
Abstract
:1. Introduction
2. Theoretical Background
2.1. Theoretical Framework
2.2. Hypothesis Framework
3. Research Methodology
3.1. Factors Influencing UHEC
3.2. STIRPAT Model
4. Results
4.1. Analysis of the Results of UHEC in China
4.2. Analyses of the Impact Factors of UHEC
Parameter Estimation Results of Panel Data Model
5. Discussion
5.1. Analysis of the Difference in UHEC in China
5.2. Different Contributions of Impact Factors Affecting UHEC
- Population factors can be classified as (i) average family size and (ii) age structure, including the presence of elderly people (65 years and above). The increasing average family size in China (FSIZE) negatively contributes to UHEC, with an effect coefficient of −0.383, as supported by Brantley [40], who found that larger households were associated with lower levels of electricity in both developed and developing countries. The coefficient of influence for the presence of older people was 0.315, which suggests that an increase in the number of older individuals will lead to an increase in UHEC. This conclusion is supported by Liu [41], whose study of 150 countries showed that younger age groups consume less than older age groups, and that population aging will, to some extent, lead to an increase in heat and electricity consumption. Fintan [22] found that households with young people (18–35 years) tended to have lower electricity consumption than households in other age groups (36–55 years or 56+). Elderly people are a group of people who do not have full-time jobs and whose physical functions are declining, have an increased need for air conditioning, and are at home for extended periods of the day [32,41].
- Economic factors can be categorised as: (i) GDP per capita and (ii) factors such as electricity prices that were not included in this study. Of these, GDP per capita ranks high in terms of the impact coefficient and significantly impacts UHEC. This result is supported by Wang [42], who found that higher GDP per capita tends to be accompanied by a higher quality of life and electricity consumption. However, Yalcintas [27] demonstrated that the price of electricity has no significant impact on household electricity consumption and is not a good indicator to study; thus, it was not included in this study.
- Table 5 shows that increasing levels of urbanisation (U) had the most significant positive contribution to UHEC, with an impact coefficient of 0.657. This result is supported by Bilgili [43], who investigated ten Asian countries and found that the impact of urbanisation on energy intensity can be long or short. An analysis of Tahsin’s research shows a panel Granger causality between energy consumption [44], urbanisation, and economic growth, and that they all influence each other. Thus, it can be seen that China’s rapid urbanisation and economic development have directly led to an increase in household electricity consumption.
- Per capita housing size is also a significant factor affecting household electricity consumption. Huang [45] identified the characteristics of high-electricity-consuming households through his study, in which he found that the larger the dwelling size (larger housing area per capita), the higher the household electricity consumption for same-same populations.
- A negative correlation between HDD and UHEC was observed under the influence of temperature. Berkouwer’s [46] study of nearly 6000 households showed that an increase in temperature and a decrease in the number of HDD days results in a 6.2% reduction in annual electricity consumption per household, which supports our conclusion. However, the impact of CDD on UHEC was not as pronounced.
- Factors such as the number of household appliances owned (AC and TV) may have different effects on household electricity consumption. For example, AC may reduce household electricity consumption to some extent, possibly because households choose to purchase and use expensive but more energy-efficient AC equipment to reduce the expenditures associated with electricity consumption [47]. However, this finding is valid only for AC, and the effect for other household appliances, such as TVs, is not significant [48].
6. Conclusions and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Classification | Variable | Variable Name | Type | Source of Data |
---|---|---|---|---|
Dependent variable | E | Urban household electricity consumption | Explained variable | China Energy Statistical Yearbook |
Demographic factor | FSIZE | Average family size (person/household) | Explanatory variable | China Statistical Yearbook |
ELDER | Presence of elderly people | Explanatory variable | China Statistical Yearbook | |
Economic factors | PGDP | Per capita gross domestic product | Explanatory Variable | China Statistical Yearbook |
Urbanisation | U | Urbanisation level in China | Explanatory variable | China demographic and employment statistics yearbook |
Household appliances | AC | Air conditioner per household | Explanatory Variable | China Statistical Yearbook |
TV | Colour TV per household | Explanatory variable | China Statistical Yearbook | |
Floor area | F | Floor area per capita | Explanatory variable | City statistical yearbooks |
Temperature | CDD | Cooling degree days | Explanatory Variable | National Weather Service China |
HDD | Heating degree days | Explanatory Variable | National Weather Service China |
Ln (FSIZE) | Ln (PGDP) | Ln (U) | Ln (AC) | Ln (TV) | Ln (F) | Ln (CDD) | Ln (HDD) | Ln (OLD) | |
---|---|---|---|---|---|---|---|---|---|
Ln (FSIZE) | 1 | ||||||||
Ln (PGDP) | −0.635 *** | 1 | |||||||
Ln (U) | −0.705 *** | 0.885 *** | 1 | ||||||
Ln (AC) | −0.311 *** | 0.433 *** | 0.422 *** | 1 | |||||
Ln (TV) | −0.243 *** | 0.120 *** | 0.231 *** | 0.566 *** | 1 | ||||
Ln (F) | −0.113 ** | 0.455 *** | 0.221 *** | 0.425 *** | 0.234 *** | 1 | |||
Ln (CDD) | −0.084 * | 0.253 *** | 0.297 *** | 0.800 *** | 0.577 *** | 0.438 *** | 1 | ||
Ln (HDD) | −0.266 *** | −0.00200 | 0.0130 | −0.440 *** | −0.269 *** | −0.304 *** | −0.613 *** | 1 | |
Ln (OLD) | −0.198 *** | 0.080 * | −0.0440 | 0.538 *** | 0.338 *** | 0.332 *** | 0.328 *** | −0.086 * | 1 |
Model (LnE) | Coefficients | Std. Error | P > |t| | Tolerance | VIF |
---|---|---|---|---|---|
C | −10.514 | 0.705 | 0.000 | 0 | 0 |
Ln (FSIZE) | −0.137 | 0.157 | 0.384 | 0.356 | 2.81 |
Ln (PGDP) | 0.452 | 0.050 | 0.000 | 0.111 | 9.03 |
Ln (U) | 0.593 | 0.134 | 0.000 | 0.106 | 9.45 |
Ln (AC) | −0.040 | 0.017 | 0.023 | 0.201 | 4.97 |
Ln (TV) | 0.221 | 0.088 | 0.013 | 0.543 | 1.84 |
Ln (F) | 0.371 | 0.083 | 0.000 | 0.455 | 2.20 |
Ln (CDD) | −0.048 | 0.010 | 0.000 | 0.221 | 4.52 |
Ln (HDD) | −0.134 | 0.018 | 0.000 | 0.502 | 1.99 |
Ln (OLD) | 0.878 | 0.019 | 0.000 | 0.470 | 2.13 |
Test Summary | Chi-Sq. Statistic | Chi-Sq. d. f. | Prob. | |
---|---|---|---|---|
Model (1) | Cross-section random | 62.622 | 9 | 0.000 |
Model (2) | Cross-section random | 52.848 | 7 | 0.000 |
Variable | Model (1) | Model (2) |
---|---|---|
Ln (U) | 0.671 *** (5.627) | 0.657 *** (5.520) |
Ln (PGDP) | 0.515 *** (15.628) | 0.535 *** (17.618) |
Ln (HDD) | −0.106 *** (−2.497) | −0.113 *** (−2.674) |
Ln (OLD) | 0.286 *** (4.841) | 0.315 *** (5.520) |
Ln (TV) | −0.182 (−1.572) | |
Ln (FSIZE) | −0.367 *** (−3.738) | −0.383 *** (−3.910) |
Ln (F) | 0.152 *** (2.682) | 0.149 *** (2.624) |
Ln (CDD) | 0.007 (0.889) | |
Ln (AC) | −0.065 *** (−2.258) | −0.073 *** (−2.580) |
C | −4.079 *** (−4.3355) | −5.202 *** (−8.333) |
Adjust R2 | 0.985 | 0.985 |
F | 776.553 | 816.978 |
P | 0.000 | 0.000 |
DW | 0.471 | 0.480 |
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Wang, Y.; Cai, W.; Hou, L.; Zhou, Z.; Bian, J. Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model. Sustainability 2022, 14, 9960. https://doi.org/10.3390/su14169960
Wang Y, Cai W, Hou L, Zhou Z, Bian J. Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model. Sustainability. 2022; 14(16):9960. https://doi.org/10.3390/su14169960
Chicago/Turabian StyleWang, Yuanping, Weiguang Cai, Lingchun Hou, Zhaoyin Zhou, and Jing Bian. 2022. "Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model" Sustainability 14, no. 16: 9960. https://doi.org/10.3390/su14169960
APA StyleWang, Y., Cai, W., Hou, L., Zhou, Z., & Bian, J. (2022). Examining the Provincial-Level Difference and Impact Factors of Urban Household Electricity Consumption in China—Based on the Extended STIRPAT Model. Sustainability, 14(16), 9960. https://doi.org/10.3390/su14169960