| Literature DB >> 35886530 |
Arif Ullah1, Kashif Raza2, Muhammad Nadeem1, Usman Mehmood3, Ephraim Bonah Agyekum4, Mohamed F Elnaggar5,6, Ebenezer Agbozo7, Salah Kamel8.
Abstract
Global environmental issues such as environmental degradation, climate change, and global warming have posed a threat to the global economy, including Pakistan. The primary source of these problems is greenhouse gas emissions. These emissions are the result of human activity. The objective of the study was to investigate the symmetric and asymmetric relationship between globalization and greenhouse gas emissions in Pakistan. The ARDL modern econometric techniques of the time series model were used. Firstly, the stationarity test favors the use of the ARDL model in this study. The BDS test result confirmed that the ARDL model has a non-linearity issue. As a result, the ARDL approach was used to test both the symmetric and asymmetric effect. The results of the asymmetric ARDL model are more robust and reliable than those of the symmetric ARDL model. According to the results of the symmetric ARDL, economic, social, and political globalization have a positive relationship with greenhouse gas emissions in both the short and long run. Furthermore, the long-run results of the asymmetric ARDL model show that positive and negative shocks of economic and political globalization have positive and negative shock effects on greenhouse gas emissions. In the long run, however, the positive shock of social globalization has a negative relationship with greenhouse gas emissions. According to the results of impulse response functions, economic globalization has a significantly more relationship with greenhouse gas emissions than social and political globalization. A policy should be developed that allows only the positive effects of globalization while prohibiting the negative effects of globalization.Entities:
Keywords: Pakistan; environmental degradation; globalization; greenhouse gas emissions; non-linear ARDL
Mesh:
Substances:
Year: 2022 PMID: 35886530 PMCID: PMC9324046 DOI: 10.3390/ijerph19148678
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1The flow path of methodology.
Correlation and descriptive analysis of the study.
| GHG | GDP | EGI | PGI | SGI | |
|---|---|---|---|---|---|
| GHG | 1.00 | 0.98 | 0.42 | 0.88 | 0.98 |
| GDP | 0.98 | 1.00 | 0.39 | 0.83 | 0.95 |
| EGI | 0.42 | 0.39 | 1.00 | 0.58 | 0.47 |
| PGI | 0.88 | 0.83 | 0.58 | 1.00 | 0.88 |
| SGI | 0.98 | 0.95 | 0.47 | 0.88 | 1.00 |
| Mean | 285.68 | 907.79 | 36.31 | 80.85 | 29.37 |
| Median | 274.65 | 878.13 | 35.25 | 82.25 | 28.45 |
| Maximum | 403.67 | 1155.36 | 43.87 | 85.55 | 42.62 |
| Minimum | 180.12 | 736.95 | 32.72 | 66.91 | 15.34 |
| Std. Dev. | 70.41 | 120.61 | 3.18 | 5.05 | 10.70 |
| Skewness | 0.11 | 0.37 | 0.95 | −1.22 | −0.02 |
| Kurtosis | 1.69 | 1.89 | 2.94 | 3.77 | 1.44 |
| Jarque-Bera | 2.05 | 2.09 | 4.18 | 7.63 | 2.84 |
| Probability | 0.36 | 0.35 | 0.12 | 0.02 | 0.24 |
| Sum | 7999.06 | 25,418.00 | 1016.71 | 2263.87 | 822.24 |
| Sum Sq. Dev. | 133,854.50 | 392,761.60 | 272.61 | 688.44 | 3093.87 |
| Observations | 28.00 | 28.00 | 28.00 | 28.00 | 28.00 |
Source: Author self-estimation and calculations.
Figure 2The trend of the series.
Results of unit root test.
| Parameters | ADF | PP | Zivot and Andrews | |||
|---|---|---|---|---|---|---|
| Level | 1st Difference | Level | 1st Difference | Level | Break | |
| LnGHG | −1.65 | −3.75 *** | −1.66 | −3.79 *** | −2.33 ** | 2010 |
| LnGDP | 0.37 | 0.31 ** | 0.38 | −3.10 ** | −2.34 ** | 2010 |
| LnEGI | −1.50 | −5.19 *** | −1.50 | −5.19 *** | −2.28 ** | 2001 |
| LnSGI | −1.05 | −4.37 *** | −0.97 | −4.22 *** | −4.16 *** | 2010 |
| LnPGI | −7.71 | −3.27 ** | −7.43 | −3.34 ** | −5.74 ** | 2000 |
Note: *** and ** represent levels of significance at 1% and 5% level, respectively.
Results of BDS non-linearity test.
| Series | D 2 | D 3 | D 4 | D 5 | D 6 |
|---|---|---|---|---|---|
| LNGHG | 0.188 *** | 0.314 *** | 0.395 *** | 0.444 *** | 0.490 *** |
| LNGDP | 0.162 *** | 0.261 *** | 0.322 *** | 0.355 *** | 0.357 *** |
| LNEGI | 0.089 *** | 0.154 *** | 0.182 *** | 0.189 *** | 0.179 *** |
| LNSGI | 0.199 *** | 0.337 *** | 0.432 *** | 0.496 *** | 0.543 *** |
| LNPGI | 0.191 *** | 0.326 *** | 0.421 *** | 0.496 *** | 0.539 *** |
Note: The BDS test based on the residuals of a VAR for all chosen variables and D denotes Dimension of the variables. *** represent levels of significance at 1%.
Bound test for Symmetric and Asymmetric ARDL.
| Models | F Statistics Value | Significance Level | Lower Bound Value | Upper Bound Value |
|---|---|---|---|---|
| Symmetric ARDL | 7.97 | 10% | 2.20 | 3.09 |
| 5% | 2.56 | 3.49 | ||
| 1% | 3.29 | 4.37 | ||
| Asymmetric ARDL | 6.80 | 10% | 1.85 | 2.85 |
| 5% | 2.11 | 3.15 | ||
| 1% | 2.62 | 3.77 |
Source: Author self-estimation and calculations.
Results of symmetric ARDL.
| Short-Run Elasticities | Long-Run Elasticities | ||||||
|---|---|---|---|---|---|---|---|
| Variable | Co-Efficient | t-Statistic | Variable | Co-Efficient | t-Statistic | ||
| ΔLnGDP | 0.62 | 3.20 | 0.00 *** | LnGDP | 0.81 | 4.84 | 0.00 *** |
| ΔLnEGI | 0.15 | 1.53 | 0.14 | LnEGI | 0.11 | 0.81 | 0.43 |
| ΔLnSGI | 0.59 | 2.05 | 0.05 ** | LnSGI | 1.25 | 2.94 | 0.01 ** |
| ΔLnPGI | 0.03 | 0.36 | 0.72 | LnPGI | 0.20 | 2.29 | 0.03 ** |
| Cointeg Eq (−1) | −0.70 | −4.48 | 0.00 *** | Constant | −5.63 | −2.80 | 0.01 ** |
| Model Selection Criteria | Diagnostics Tests | ||||||
| Log-likelihood | 67.20 | JB Normality | 6.88 [0.13] | ||||
| AIC | −4.53 | χ2 ARCH | 0.36 [0.55] | ||||
| BIC | −4.24 | χ2 RESET | 0.98 [0.33] | ||||
| HQ | −4.45 | χ2 LM | 0.23 [0.79] | ||||
| Adjusted R2 | 0.99 | ||||||
| Model Specification | ARDL (1,0,0,0,0) | ||||||
Note: *** and ** represent levels of significance at 1%, 5% and 10%, respectively.
Results of asymmetric ARDL.
| Short-Run Elasticities | Long-Run Elasticities | ||||||
|---|---|---|---|---|---|---|---|
| Variable | Co-Efficient | t-Statistic | Variable | Co-Efficient | t-Statistic | ||
| ΔLnGDP+ | 0.54 | 5.86 | 0.00 *** | LnGDP+ | 1.09 | 5.40 | 0.00 *** |
| ΔLnGDP− | −0.29 | −0.74 | 0.48 | LnGDP− | −2.65 | −2.06 | 0.07 ** |
| ΔLnEGI+ | −0.07 | −1.28 | 0.23 | LnEGI+ | 0.33 | 1.51 | 0.16 |
| ΔLnEGI− | −0.06 | −1.01 | 0.33 | LnEGI− | −0.03 | −0.44 | 0.67 |
| ΔLnSGI+ | −0.14 | −4.07 | 0.00 *** | LnSGI+ | −0.14 | −1.62 | 0.14 |
| ΔLnSGI− | 24.65 | 14.57 | 0.00 *** | LnSGI− | 13.78 | 1.29 | 0.23 |
| ΔLnPGI+ | −0.90 | −4.40 | 0.00 *** | LnPGI+ | 0.52 | 1.28 | 0.23 |
| ΔLnPGI− | 0.22 | 0.45 | 0.66 | LnPGI− | −2.32 | −1.66 | 0.13 |
| CointEq (−1) | −0.87 | −11.96 | 0.00 *** | Constant | 5.34 | 66.17 | 0.00 *** |
| Model Selection Criteria | Diagnostics Test | ||||||
| Log-likelihood | 96.19 | JB Normality | 0.96 [0.61] | ||||
| AIC | −6.17 | χ2 ARCH | 0.10 [0.74] | ||||
| BIC | −5.39 | χ2 RESET | 1.18 [0.30] | ||||
| HQ | −5.95 | χ2 LM Test | 2.92 [0.11] | ||||
| Adjusted R2 | 0.99 | ||||||
| Model Specification | ARDL (1,1,1,1,0,1,1,0,1) | ||||||
Note: *** and ** represent levels of significance at 1% and 5% respectively.
Figure 3Symmetric ARDL CUSUM test.
Figure 4Symmetric ARDL CUSUM Squares test.
Figure 5Asymmetric ARDL CUSUM test.
Figure 6Asymmetric ARDL CUSUM of Squares test.
Figure 7Accumulative dynamic multiplier plots.
Figure 8Response to Cholesky one S.D. Innovations ± 2 S.E.