Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach
The global energy system is highly vulnerable to climate variability and change. This results in a vast range of impacts on the energy demand sector and production and supply channels. This article aims to estimate the impacts of variables such as heating and cooling temperatures, income, population...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Climate |
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| Online Access: | https://www.mdpi.com/2225-1154/10/10/142 |
| _version_ | 1797474170435534848 |
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| author | Chukwuemeka Chinonso Emenekwe Nnaemeka Vincent Emodi |
| author_facet | Chukwuemeka Chinonso Emenekwe Nnaemeka Vincent Emodi |
| author_sort | Chukwuemeka Chinonso Emenekwe |
| collection | DOAJ |
| description | The global energy system is highly vulnerable to climate variability and change. This results in a vast range of impacts on the energy demand sector and production and supply channels. This article aims to estimate the impacts of variables such as heating and cooling temperatures, income, population, and price on residential electricity demand in G7 countries. Methodologically, this study uses the second-generation panel unit root and cointegration approaches (which are robust in the presence of cross-sectional dependence), a panel fixed effects model with Driscoll–Kraay standard errors, and a novel method of moments quantile regression (MM-QR) to determine long-run elasticities. The results suggest that the residential electricity demand of G7 countries is statistically and positively responsive to cold days rather than hot days. This study also presents some policy-relevant issues based on the results. |
| first_indexed | 2024-03-09T20:26:18Z |
| format | Article |
| id | doaj.art-90b8c99d2a90434eaba1e078bb1615f1 |
| institution | Directory Open Access Journal |
| issn | 2225-1154 |
| language | English |
| last_indexed | 2024-03-09T20:26:18Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Climate |
| spelling | doaj.art-90b8c99d2a90434eaba1e078bb1615f12023-11-23T23:34:33ZengMDPI AGClimate2225-11542022-09-01101014210.3390/cli10100142Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression ApproachChukwuemeka Chinonso Emenekwe0Nnaemeka Vincent Emodi1Department of Economics and Development Studies, Alex Ekwueme Federal University Ndufu-Alike, P.M.B. 1010, Abakaliki 480213, Ebonyi State, NigeriaUQ Business School, University of Queensland, Brisbane, QLD 4072, AustraliaThe global energy system is highly vulnerable to climate variability and change. This results in a vast range of impacts on the energy demand sector and production and supply channels. This article aims to estimate the impacts of variables such as heating and cooling temperatures, income, population, and price on residential electricity demand in G7 countries. Methodologically, this study uses the second-generation panel unit root and cointegration approaches (which are robust in the presence of cross-sectional dependence), a panel fixed effects model with Driscoll–Kraay standard errors, and a novel method of moments quantile regression (MM-QR) to determine long-run elasticities. The results suggest that the residential electricity demand of G7 countries is statistically and positively responsive to cold days rather than hot days. This study also presents some policy-relevant issues based on the results.https://www.mdpi.com/2225-1154/10/10/142residential electricity consumptiontemperature variationheating degree dayscooling degree daysDriscoll–Kraay standard errorspanel data |
| spellingShingle | Chukwuemeka Chinonso Emenekwe Nnaemeka Vincent Emodi Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach Climate residential electricity consumption temperature variation heating degree days cooling degree days Driscoll–Kraay standard errors panel data |
| title | Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach |
| title_full | Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach |
| title_fullStr | Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach |
| title_full_unstemmed | Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach |
| title_short | Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach |
| title_sort | temperature and residential electricity demand for heating and cooling in g7 economies a method of moments panel quantile regression approach |
| topic | residential electricity consumption temperature variation heating degree days cooling degree days Driscoll–Kraay standard errors panel data |
| url | https://www.mdpi.com/2225-1154/10/10/142 |
| work_keys_str_mv | AT chukwuemekachinonsoemenekwe temperatureandresidentialelectricitydemandforheatingandcoolinging7economiesamethodofmomentspanelquantileregressionapproach AT nnaemekavincentemodi temperatureandresidentialelectricitydemandforheatingandcoolinging7economiesamethodofmomentspanelquantileregressionapproach |