| Literature DB >> 33184786 |
Sajid Iqbal1, Ahmad Raza Bilal2, Mohammad Nurunnabi3, Wasim Iqbal4, Yazeed Alfakhri5, Nadeem Iqbal6.
Abstract
During the COVID-19 outbreak, managing energy consumption and CO2 emission remained a serious problem. The previous literature rarely solved this real-time issue, and there is a lack of public research proposing an effective way forward on it. However, the study examines the impact of the COVID-19 outbreak on energy consumption and CO2 emission. The design of the study is quantitative, and the data is acquired from different online databases. The model of the study is inferred by using panel unit root test and ARDL test. The robustness of study findings was checked through panel quantile regression. The findings highlighted that the COVID-19 outbreak is negatively significant with energy consumption and CO2 emission. The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection. The study also contributed in knowledge by suggesting the novel insight into CO2 emission and energy consumption patterns during COVID-19 pandemic and recommended to consider renewable energy transition methods as an opportunity for the society. For a more effective management of energy consumption and environmental pollution, country-specific measures are suggested to be taken, and the national government should support the concerned public departments, ministries and private organizations on it. To the best of our study, this is one of the pioneer studies studying this novel link and suggesting the way forward on recent topicality.Entities:
Keywords: CO2 emission; COVID-19 outbreak; Energy consumption; Energy transition; Renewable energy
Year: 2020 PMID: 33184786 PMCID: PMC7659900 DOI: 10.1007/s11356-020-11462-z
Source DB: PubMed Journal: Environ Sci Pollut Res Int ISSN: 0944-1344 Impact factor: 4.223
Descriptive statistics
| Summary statistics, 13 March 2020–30 April 2020 | ||||
|---|---|---|---|---|
| Variable | Mean | SD | Minimum | Maximum |
| CO2 emission | 0.2857 | 0.0188 | 0.0701 | 0.1543 |
| TEC | 0.6658 | 0.0073 | 0.0165 | 0.5491 |
| EC1 | 0.3471 | 0.0016 | 0.0111 | 0.4292 |
| EC2 | 0.2994 | 0.0066 | 0.0227 | 0.3572 |
| COVID-19 lockdown1 | 0.2562 | 0.0714 | 0.0066 | 0.3703 |
n(Observations) = 48
1This variable is measured in terms of numbers of days in lockdown in the study context
Province level CO2 emission reduction in major CO2 emitting sectors
| Provinces | Σ CO2 reduction | Oil and gas | Electricity | Cement | Transport |
|---|---|---|---|---|---|
| Sindh | 6.5 | 2.0 | 1.7 | 1.3 | 1.8 |
| Punjab | 11.9 | 6.6 | 1.2 | 1.0 | 2.3 |
| KPK | 3.0 | 1.5 | 0.5 | 0.0 | 1.0 |
| Baluchistan | 2.8 | 1.7 | 0.1 | 0.0 | 1.0 |
Fig. 1Energy consumption and CO2 emission during the COVID-19 outbreak. (Source: macrotrends.net)
Fig. 2WTI crude oil futures (source: investing.com)
Panel unit root test
| Constructs | LLC | FADF | FPP | IPS | Stationary level |
|---|---|---|---|---|---|
| COVID-19 | 1.97 | 16.96 | 11.07 | 2.07 | - |
| − 0.44* | 41.77* | 86.1* | − 0.12 | ||
| CO2 emission | 1.53 | 12.36 | 5.47 | 1.26 | - |
| − 5.75* | 57.89* | 30.01* | − 4.67* | ||
| TEC | 1.19 | 2.56 | 6.05 | 1.11 | |
| − 3.44* | 45.55* | 49.08* | − 2.89* | ||
| ECI | 2.56 | 12.75 | 7.13 | 2.13 | |
| − 6.66* | 71.01* | 32.3* | − 4.12* | ||
| EC2 | 7.95 | 60.34* | 57.40* | 7.01 | |
| − 2.06* | 22.87* | 22.62* | − 1.53* |
Source: authors’ calculation using E-Views 10 software
LLC Levin–Lin–Chu test, FADF Fisher-augmented Dickey–Fuller test, FPP Fisher Phillips–Perron test, IPS Im–Pesaran–Shin test
*Significance at the 5% level
Autoregressive distributed lag (ARDL) test
| Variable | Coefficient | Std. error | Prob.* | |
|---|---|---|---|---|
| C | 11.26* | 65.31 | 6.07 | 0.000 |
| COVID-19 | 6.24* | 12.18 | 3.59 | 0.000 |
| CO2 emission | − 8.55* | 33.81 | − 4.24 | 0.000 |
| TEC | − 3.13* | 0.073 | − 3.33 | 0.001 |
| EC1 | − 1.27* | 0.675 | − 1.83 | 0.000 |
| EC2 | − 2.12* | 0.009 | − 0.93 | 0.000 |
| 0.59 | Mean-dependent variance | 1.41 | ||
| Adjusted | 0.20 | S.D. dependent variance | 77.12 | |
| Standard error | 16.04 | Akaike info criterion | 8.28 | |
| Log LH | − 364.19 | Hannan–Quinn criterion | 10.29 | |
| Prob ( | 0.011 | Durbin–Watson | 1.99 | |
*p value < 0.05 as level of significance
PQR test
| Variable | Coefficient | SE | Sig. | Pseudo | Sparsity | |
|---|---|---|---|---|---|---|
| COVID-19 | 4.16 | 3.76 | 2.22 | 0.000 | 0.71 | 1311.91 |
| CO2 emission | − 2.76* | 0.04 | − 0.67 | 0.000 | (0.000) | |
| TEC | − 1.88* | 0.07 | − 0.41 | 0.000 | ||
| EC1 | − 0.65* | 0.00 | − 0.17 | 0.000 | ||
| EC2 | − 0.73* | 0.31 | − 0.11 | 0.000 | ||
| C | 122.64 | 233.19 | 0.81 | 0.026 | ||
| COVID-19 | 3.03 | 2.07 | 2.69 | 0.000 | 0.87 | 1010.32 |
| CO2 emission | − 2.12* | 0.06 | − 0.51 | 0.000 | (0.000) | |
| TEC | − 1.05* | 0.02 | − 0.34 | 0.000 | ||
| EC1 | − 0.44* | 0.11 | − 0.47 | 0.000 | ||
| EC2 | − 0.67* | 0.21 | − 0.01 | 0.000 | ||
| C | 174.01 | 217.01 | 0.85 | 0.000 | ||
| COVID-19 | 5.33 | 2.26 | 2.89 | 0.000 | 0.77 | 1275.09 |
| CO2 emission | − 3.21* | 0.09 | − 2.91 | 0.000 | (0.000) | |
| TEC | − 2.24* | 0.03 | − 2.14 | 0.000 | ||
| EC1 | − 1.45* | 0.05 | − 0.18 | 0.000 | ||
| EC2 | 0.79* | 0.00 | − 0.56 | 0.000 | ||
| C | 167.34 | 202.87 | 0.77 | 0.000 | ||
| COVID-19 | 4.68 | 2.98 | 3.18 | 0.000 | 0.73 | 1405.00 |
| CO2 emission | − 2.55* | 0.01 | − 0.10 | 0.000 | (0.000) | |
| TEC | − 2.41* | 0.00 | − 0.49 | 0.000 | ||
| EC1 | − 1.01* | 0.05 | − 0.32 | 0.000 | ||
| EC2 | − 0.68* | 0.00 | − 0.50 | 0.000 | ||
| C | 101.34 | 157.48 | 0.03 | 0.000 | ||
| COVID-19 | 8.19 | 4.44 | 3.00 | 0.000 | 0.76 | 2178.04 |
| CO2 emission | − 4.56* | 1.30 | − 0.18 | 0.000 | (0.000) | |
| TEC | − 2.34* | 1.21 | − 0.23 | 0.000 | ||
| EC1 | − 1.13* | 0.08 | − 0.54 | 0.000 | ||
| EC2 | − 1.07* | 0.02 | − 0.39 | 0.000 | ||
| C | 89.99 | 100.77 | 0.47 | 0.000 | ||
*p value < 0.05 as level of significance
Fig. 3Changes in global oil price during COVID-19 shock (source: countryeconomy.com)