Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach
Abstract
:1. Introduction
2. Review of Related Empirical Literature
3. Model Specification, Data, and Method of Estimation
3.1. Selection of the Variables and Hypotheses
3.2. Data Sources
3.3. Estimation Methods
3.3.1. Normality Test
3.3.2. Cross-Sectional Dependence Test
3.3.3. Unit Root Test
3.3.4. Cointegration Tests
3.3.5. Parameter Estimates
4. Empirical Results and Discussion
4.1. Results from Preliminary Tests
4.1.1. Normality Test
4.1.2. Cross-Sectional Dependence Test
4.1.3. Unit Root Test
4.1.4. Cointegration Test
4.1.5. Model Selection Tests
4.2. Empirical Results from Panel Regression Techniques
4.2.1. Heating Effect and Electricity Demand
4.2.2. Cooling Effect and Electricity Demand
4.2.3. Effects of Covariates
4.3. Further Discussion of the Results
5. Conclusions, Policy Relevance, and Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Authors | Objective | Time Period and Sample | Methodology | Key Explanatory Variables | CHE * | Key Findings | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ET | CE | QE | CD | 1UR | 2UR | 1CO | 2CO | DD | T | Y | P | Pr | |||||
Yating et al. (2019) | To investigate the effects of climate change on electricity demand among Chinese households | 1980–1999 2080–2099 China | Panel data: fixed effects | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | On cold days, an increase in temperature of 1 °C lowers electricity consumption by 2.8%; on warm days, an increase in temperatures of 1 °C increases electricity consumption by 14.5%. As income rises, the sensitivity of households to extreme weather conditions does not change for hotter summer days, but it does rise for colder winter days. |
Thornton et al. (2016) | To examine the influence of temperature plays on electricity demand variability and extremes in Great Britain | 1975–2013 Great Britain | Time series: trend analysis | ✓ | ✗ | ✗ | ✗ | — | — | — | ✗ | ✓ | ✓ | ✓ | ✓ | ✗ | Mean electricity demand exhibits low-frequency variability, linked primarily to the influence of variable socioeconomic factors. However, they also show that electricity demand and temperature have a high negative correlation (r= −0.90) when the variability of socioeconomic factors is removed. Seasonal variations in electricity demand lead to an increased correlation between temperature and electricity demand. Taking annual temperature and demand cycles out of the equation, we get a correlation, r, of 0.60. Temperature and demand are closely linked in winter, with a 1% rise in demand for electricity for every 1 °C decrease in daily temperature. |
Emodi et al. (2018) | To investigate the short- and long-run effects of temperature variations due to climate change on energy demand | 1999–2014 Six Australian states and one territory | Time series: autoregressive distributed lag (ARDL) | ✓ | ✗ | ✗ | ✓ | — | — | — | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Increasing temperature will increase the summer peak demand for electricity, whereas the demand in the winter is projected to rise. Additionally, in some Australian states, there was a significant relationship between temperature-induced electricity demand and income, price, and population. |
Aroonruengsawat and Auffhammer (2011) | To analyze how household electricity usage differs among climate zones in response to temperature | 2003–2006 California | Panel data: fixed effects | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✓ | ✗ | Electricity demand rises substantially across all climate zones as temperatures rise |
Pilli-Sihvola et al. (2010) | To investigate the impacts of a warmer climate on heating and cooling demand in five (northern, central, and southern) European countries | 1985–2008 Finland, Germany, the Netherlands, France, and Spain | Time series: multivariate autoregression | ✓ | ✗ | ✗ | ✓ | — | — | — | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ | In Central and Northern Europe, the reduction in heating due to climate change predominates, and as a result, electricity costs will decrease. In Southern Europe, climate change and the resulting increase in cooling and electricity demand exceeds the decline in heating demand. Consequently, costs also increase. |
Du et al. (2020) | To investigate how increasing income of Chinese residents affects the climate sensitivity of electricity demand | 2005–2015 278 cities in China | Panel data: partially linear functional coefficient model | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | Due to climate change, residential electricity usage is more likely to increase in hot weather than in cold weather. Accounting for socioeconomic factors, the marginal effect of CDD on electricity consumption first increases with an increase in income. However, when income increases further, the marginal increase curve turns flat. |
Narayan et al. (2007) | To analyze the impact of income and price on electricity demand in the G7 countries | 1978–2003 G7 countries | Panel data: panel cointegration | ✓ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | Results from the G7 countries reveal that income and price are critical factors in the demand for electricity caused by temperature. |
Tol et al. (2012) | To explore the impact of climate change on cross-country energy use | 1978–2002 A panel of 62 countries | Panel data: corrected least squares dummy variable (LSDVC) | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ | Electricity demand declines with increasing temperatures due to a reduction in the demand for energy for heating, although the rate of reduction decreases as temperature increases. Temperature does not affect the demand for cooling energy. |
Li et al. (2018) | To estimate the climatic impacts on residential energy consumption in China | 2009–2014 30 provinces in China | Panel data: fixed effects | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | A warmer summer would have a more significant effect than a colder winter, meaning an increase in annual electricity usage due to global warming. |
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Variable Abbreviation | Variable Description | Variable Source |
---|---|---|
ELD | Total residential electricity consumption (in PJ) (1 GWh = 0.0036 PJ) | International Energy Agency [41] |
HDD | Heating degree days using plain temperature at 2 m elevation at the temperature reference point of 18.3 °C and frequency of 6 hours | World Average Degree Days Database [42] |
CDD | Cooling degree days using plain temperature at 2 m elevation at the temperature reference point of 18.3 °C and frequency of 6 hours | |
GDP | Expenditure-side real GDP at chained PPPs (in million 2017 US$) | Penn World Table version 10.0 [46] |
POP | Total population (in thousands) | FAOSTAT [48] |
PRC | Domestic electricity prices in the IEA, excluding taxes (US¢/kWh) | IEA, UK Department of Business, Energy, & Industrial Services [47] |
Variable | Mean | Median | Standard Deviation | Maximum | Minimum |
---|---|---|---|---|---|
lnELD | 6.457 | 6.199 | 0.880 | 8.557 | 5.246 |
lnHDD | 9.464 | 9.399 | 0.263 | 10.049 | 8.956 |
lnCDD | 7.011 | 6.992 | 0.766 | 8.101 | 4.883 |
lnGDP | 14.920 | 14.729 | 0.754 | 16.755 | 13.703 |
lnPOP | 11.281 | 11.046 | 0.656 | 12.679 | 10.223 |
lnPRC | 2.443 | 2.484 | 0.486 | 3.303 | 1.552 |
Test | Source | Description |
---|---|---|
Shapiro–Wilk | Shapiro–Wilk [49] | Checks for normality of the panel model |
Skewness/Kurtosis | D’Agostino and Belanger [50] | Check for normality based on combining skewness and kurtosis |
Cross-sectional dependence | Breusch and Pagan [51], Pesaran [52] Pesaran [53] | Check for the presence of cross-sectional dependence |
Panel unit root | Pesaran [52] | Checks for the presence of unit roots |
Westerlund panel cointegration | Westerlund [19] | Checks for cointegration based on the presence of error correction for individual model cross-sections and the whole panel |
Mundlak | Mundlak [54] | Check for individual heterogeneity and informs suitability of random or fixed effects model |
Skewness/Kurtosis Tests | Shapiro–Wilk Test | |||
---|---|---|---|---|
Variable | Skewness | Kurtosis | Prob > Chi2 | Prob > z |
lnELD | 0.000 | 0.066 | 0.000 *** | 0.000 *** |
lnHDD | 0.004 | 0.455 | 0.016 ** | 0.000 *** |
lnCDD | 0.003 | 0.623 | 0.015 ** | 0.000 *** |
lnGDP | 0.000 | 0.409 | 0.000 *** | 0.000 *** |
lnPOP | 0.000 | 0.768 | 0.003 *** | 0.000 *** |
lnPRC | 0.063 | 0.000 | 0.000 *** | 0.000 *** |
Test | Statistic | p-Value |
---|---|---|
Breusch and Pagan [51] LM test statistic | 58.41 *** | 0.000 |
Pesaran [52] test statistic | 12.09 *** | 0.000 |
Pesaran [53] LM CD + | 4.706 *** | 0.000 |
Variables | IPS | CIPS | |||||||
---|---|---|---|---|---|---|---|---|---|
At Level | At First Difference | At Level | At First Difference | ||||||
C | C&T | C | C&T | C | C&T | C | C&T | Decision | |
lnELD | −0.9962 | 5.1757 | −6.1621 *** | −7.9973 *** | −1.849 | −2.327 | −4.424 *** | −4.585 *** | I(1) |
lnHDD | −4.0056 *** | −3.2286 *** | −11.1058 *** | −9.5801 *** | −2.687 *** | −3.124 *** | −4.650 *** | −4.577 *** | I(1) |
lnCDD | −4.3051 *** | −3.8447 *** | −13.8722 *** | −12.3010 *** | −2.655 *** | −3.158 *** | −5.402 *** | −5.379 *** | I(1) |
lnGDP | −1.1016 | 0.4438 | −4.9474 *** | −4.1409 *** | −2.209 | −2.003 | −3.078 *** | −2.955 ** | I(1) |
lnPOP | 2.9720 | 5.6314 | −5.5822 *** | −10.4249 *** | −2.940 *** | −1.775 | −3.228 *** | −4.304 *** | I(1) |
lnPRC | 0.9342 | 0.2908 | −3.9564 *** | −2.3453 *** | −2.044 | −2.869 * | −3.037 *** | −3.243 *** | I(1) |
Error Correction Based | |||||
---|---|---|---|---|---|
Model Specifications | Gt (Robust p-Value) | Ga (Robust p-Value) | Pt (Robust p-Value) | Pa (Robust p-Value) | |
1 | lnELD, lnHDD, lnGDP, lnPOP, lnPRC | −2.725 *** (0.000) | −6.618 (0.240) | −6.695 ** (0.020) | −7.924 * (0.070) |
2 | lnELD, lnCDD, lnGDP, lnPOP, lnPRC | −2.946 ** (0.030) | −9.310 (0.100) | −9.346 *** (0.000) | −12.812 *** (0.010) |
Model Specifications | χ2 (4) | Prob > χ2 | |
---|---|---|---|
1 | lnELD, lnHDD, lnGDP, lnPOP, lnPRC | 62.35 *** | 0.000 |
2 | lnELD, lnCDD, lnGDP, lnPOP, lnPRC | 55.97 *** | 0.000 |
MM-QR | ||||||
---|---|---|---|---|---|---|
Variables | D–K | Q.10th | Q.25th | Q.50th | Q.75th | Q.90th |
(1) | (2) | (3) | (4) | (5) | (6) | |
lnHDD | 0.260 ** | 0.210 | 0.237 ** | 0.265 *** | 0.285 *** | 0.300 ** |
(0.082) | (0.158) | (0.117) | (0.095) | (0.101) | (0.117) | |
lnGDP | 0.442 *** | 0.454 *** | 0.448 *** | 0.441 *** | 0.436 *** | 0.433 *** |
(0.056) | (0.082) | (0.061) | (0.049) | (0.053) | (0.061) | |
lnPOP | 0.532 ** | 0.804 *** | 0.659 *** | 0.505 *** | 0.395 ** | 0.312 |
(0.159) | (0.282) | (0.209) | (0.169) | (0.180) | (0.210) | |
lnPRC | −0.076 ** | −0.163 *** | −0.117 *** | −0.068 ** | −0.032 | −0.005 |
(0.030) | (0.044) | (0.033) | (0.026) | (0.028) | (0.033) | |
Constant | −8.421 *** | −11.052 *** | −9.650 *** | −8.159 *** | −7.089 *** | −6.279 *** |
(1.244) | (2.748) | (2.036) | (1.652) | (1.756) | (2.049) | |
Obs. | 182 | 182 | 182 | 182 | 182 | 182 |
Groups | 7 | 7 | 7 | 7 | 7 | 7 |
MM-QR | ||||||
---|---|---|---|---|---|---|
Variables | D–K | Q.10th | Q.25th | Q.50th | Q.75th | Q.90th |
lnCDD | 0.000 | −0.014 | −0.006 | 0.000 | 0.008 | 0.013 |
(0.025) | (0.041) | (0.030) | (0.026) | (0.029) | (0.034) | |
lnGDP | 0.435 *** | 0.486 *** | 0.458 *** | 0.436*** | 0.409 *** | 0.393 *** |
(0.052) | (0.080) | (0.059) | (0.052) | (0.057) | (0.067) | |
lnPOP | 0.535 *** | 0.725 *** | 0.621 *** | 0.537 *** | 0.437 ** | 0.378 * |
(0.138) | (0.267) | (0.199) | (0.173) | (0.192) | (0.224) | |
lnPRC | −0.062 * | −0.145 *** | −0.100 *** | −0.063 ** | −0.019 | 0.007 |
(0.031) | (0.042) | (0.031) | (0.027) | (0.029) | (0.034) | |
Constant | −5.926 *** | −8.593 *** | −7.129 *** | −5.950 *** | −4.536 *** | −3.711 ** |
(0.873) | (2.191) | (1.628) | (1.413) | (1.562) | (1.826) | |
Obs. | 182 | 182 | 182 | 182 | 182 | 182 |
Groups | 7 | 7 | 7 | 7 | 7 | 7 |
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Emenekwe, C.C.; Emodi, N.V. Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach. Climate 2022, 10, 142. https://doi.org/10.3390/cli10100142
Emenekwe CC, Emodi NV. Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach. Climate. 2022; 10(10):142. https://doi.org/10.3390/cli10100142
Chicago/Turabian StyleEmenekwe, Chukwuemeka Chinonso, and Nnaemeka Vincent Emodi. 2022. "Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach" Climate 10, no. 10: 142. https://doi.org/10.3390/cli10100142