Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman
Abstract
:1. Introduction
1.1. Greenhouse Gas Emissions in Oman
- Energy production: A major portion of Oman’s greenhouse gas emissions come from the energy sector. Because the nation’s economy and electricity generation are mostly dependent on oil and natural gas, fossil fuels are burned in power plants and other industrial processes, which contributes to the atmospheric release of large amounts of carbon dioxide (CO2) [6].
- Transportation: Due to Oman’s expanding population and economy, there are more cars on the road, which means that gasoline- and diesel-powered cars are the main contributors to CO2 emissions. The reliance on private vehicles is additionally exacerbated by the absence of a well-developed public transportation system [5].
- Industrial processes: As a byproduct of their operation, industrial processes, including the manufacture of cement, smelting metal, and producing petrochemicals, emit greenhouse gases. CO2 and other gases including nitrous oxide (N2O) and methane (CH4) are among these emissions [7].
- Waste management is essential to reducing methane emissions. Organic materials that are improperly disposed of as solid trash in landfills may break down anaerobically, producing methane. However, it is possible to reduce these emissions by putting in place appropriate waste management and recycling procedures [8,9].
- 5.
- Reductions in carbon sinks and the release of carbon stored in soils and plants can be the outcomes of changes in land use, such as urbanization and deforestation. This drop in biodiversity could have a substantial effect on the climate and ecology. The ecosystem and climate may be significantly impacted by this decline in carbon sinks [12].
- 6.
- Emissions of greenhouse gases can rise due to improper wastewater treatment system management. Better wastewater treatment systems can reduce methane emissions and improve the overall health of the environment [13].
1.2. Causes of Air Pollution in Oman
- Industrial emissions: Processes used in the extraction of oil and gas, the production of petrochemicals, and other heavy industries discharge a range of air pollutants into the environment. These emissions include, but are not limited to, particulate matter, sulfur dioxide (SO2), nitrogen oxides (NOx), and volatile organic compounds (VOCs).
- Vehicle emissions: The increasing number of cars on the road exacerbates air pollution. Both gas-powered and diesel-powered vehicles emit various pollutants, such as NOx, particulate matter, carbon monoxide (CO2), and volatile organic compounds.
- Infrastructure building and development: As cities get more populated, there may be an increase in dust and particulate matter in the air. These particles can cause respiratory problems and reduce air quality.
- Natural sources: Dust storms can have a major impact on air pollution, particularly in Oman’s arid climate. Sand and dust particles are carried into the atmosphere and, once settled, have the potential to impact air quality.
- Maritime transport: Oman has a sizable shipping sector due to its advantageous location close to the Arabian Sea and the Gulf of Oman. The air quality around the shore is impacted by ship emissions, which might include sulfur dioxide, SO2, and particulate matter.
- Waste management: Open burning of trash and inefficient waste disposal can release pollutants back into the atmosphere. Particularly in certain less developed areas, this is a worry.
- Oil and gas operations: As a significant producer of both, Oman may emit greenhouse gases and pollutants during the extraction and processing of these resources.
- Refineries and petrochemical facilities: The refining and petrochemical of natural gas and oil can discharge a number of airborne pollutants.
- Agricultural practices: Ammonia (NH3) and other chemicals can be released back into the atmosphere during the use of fertilizers and pesticides in agriculture.
- Power generation: Pollutants may be released back into the atmosphere when fossil fuels are burned to create energy. To lessen this cause of pollution, Oman is attempting to diversify its energy sources, particularly through renewable energy initiatives.
2. Related Literature
2.1. Economic Growth and Air Pollution
2.2. Urbanization and Air Pollution
2.3. Energy Consumption and Air Pollution
3. Model Specification, Data, and Econometric Methodology
3.1. Model Specification
- Model 1:
- Model 2:
3.2. Data Analysis
- Economic growth (RGDPc): Denoted as RGDPc, it is the dependent variable in this study. It represents the real gross domestic product (GDP) per capita in Oman. It measures the average economic output generated by each individual in the country. It is a key indicator of the standard of living and economic well-being of a nation’s population.
- Carbon dioxide emissions (CO2): Denoted as CO2, they are measured in metric tons per capita. They represent the total amount of carbon dioxide gas released into the atmosphere per person in Oman. CO2 emissions are a significant environmental variable. They are a measure of the carbon footprint associated with economic activities, particularly energy production and consumption. An increase in CO2 emissions is often linked to industrialization and economic growth.
- Nitrogen dioxide emissions (N2O): Denoted as N2O, they are measured in kilotons and represent the amount of nitrous oxide gas emitted from fossil fuel consumption in Oman. N2O emissions are another important environmental variable. Nitrous oxide is a potent greenhouse gas and air pollutant. It is released from various human activities, including the burning of fossil fuels. Tracking N2O emissions is crucial for assessing the environmental impact of economic activities.
- Energy use (ENGU): Denoted as ENGU, it is measured in kilograms of oil equivalents (kgoe) per capita in Oman. ENGU represents the amount of energy consumed by an individual in Oman, typically expressed in terms of the energy equivalents of oil. It reflects the energy demand of the population and is a crucial variable when examining the relationship between energy consumption and economic growth.
- Percentage of urban population (URP): Denoted as URP, it represents the proportion of Oman’s total population living in urban areas. It is expressed as a percentage. URP is a demographic variable that reflects the level of urbanization in Oman. It indicates the extent to which the population is concentrated in urban centers as opposed to rural areas. Urbanization is often associated with economic development, as cities tend to be hubs for economic activities and job opportunities.
3.3. Econometric Methodology
- Dynamic ordinary least squares (DOLS)
- ΔYt is the dependent variable at time t;
- ΔXt is/are the independent variable(s) at time t;
- α is the intercept;
- β is/are the coefficient(s) of the independent variable(s);
- εt is the error term at time t.
- Johansen cointegration test
- Trace statistic (λ_trace): This statistic evaluates the null hypothesis, which suggests that the number of cointegration relationships is less than or equal to a specified value (r). It aids in determining the presence of cointegration within the dataset.
- Maximum eigenvalue statistic (λ_max): This statistic assesses the null hypothesis that the number of cointegration relationships precisely equals a specified value (r).
- T represents the sample size, denoting the number of observations;
- Λ is the product of the (1 − p_i) terms, where p_i signifies the eigenvalues of the estimated VAR model;
- Λ_max denotes the largest eigenvalue within the estimated VAR model.
- The error correction model (ECM)
4. Results and Discussion
4.1. Descriptive Statistics
4.2. Results of DOLS
- Model 1: economic growth (RGDPC)
- The coefficient of Ln CO2 is positive and highly significant (t-statistic = 8.022319, prob. = 0.0000), indicating that there is a positive association between carbon dioxide emissions (Ln CO2) and real GDP per capita (RGDPc). This suggests that as CO2 emissions increase, RGDPc also tends to increase, potentially highlighting a link between economic expansion and increased CO2 emissions (the result is consistent with the results of [66,67].
- The coefficient for Ln N2O is negative and highly significant (t-statistic = −10.98754, prob. = 0.0000). This indicates a negative impact of nitrous oxide (N2O) emissions on RGDPc. A decrease in N2O emissions appears to be associated with higher per capita GDP.
- The coefficient for Ln URP is positive and highly significant (t-statistic = 19.76856, prob. = 0.0000), indicating a positive relationship between urban population (URBN) and RGDPc. This suggests that urbanization has a significant and positive impact on economic growth.
- The coefficient for Ln ENGU is positive but not statistically significant (t-statistic = 0.982604, prob. = 0.3342). This suggests that energy use (ENGU) may not have had a substantial impact on Oman’s economic growth during the period under study.
- Model 2: air pollution (N2O)
- Ln RGDPC: The coefficient for Ln RGDPC is positive and highly significant (t-statistic = 7.320290, prob. = 0.0000), indicating a positive association between real GDP per capita (RGDPC) and nitrous oxide (N2O) emissions. This supports the environmental Kuznets curve (EKC) hypothesis, suggesting that economic growth initially leads to an increase in pollution before eventually decreasing it.
- Ln (RGDPcit)2: The coefficient for Ln RGDPC2 (quadratic term) is negative and highly significant (t-statistic = −5.511561, prob. = 0.0000). This quadratic term reflects the curvature in the EKC relationship, implying that, as economic growth reaches higher levels, the impact on reducing N2O emissions becomes more pronounced.
- Ln CO2, Ln URP, and Ln ENGU: These variables also show significant coefficients, suggesting their influence on N2O emissions.
4.3. Unit Root Test
- Criteria overview: The table presents various lag order selection criteria, including log likelihood (LogL), sequential modified LR (LR), final prediction error (FPE), Akaike information criterion (AIC), Schwarz information criterion (SC), and Hannan–Quinn information criterion (HQ). These criteria help determine the optimal lag order for a VAR model.
- Lag order selection: Based on the different criteria, it is clear that lag three appears to be the ideal choice for the VAR model. This is supported by the asterisks (*) in the table, indicating that lag three is the selected order by these criteria. Specifically, LR, FPE, AIC, and SC all point to lag three as the preferred choice.
4.4. Cointegration Test Results
4.4.1. Results of Johansen Cointegration Test
- Optimal lag length: Before conducting the cointegration test, the optimal lag length was determined using the minimum Akaike information criterion (AIC) and Schwarz criterion (SC) from the estimate of the unconstrained vector autoregression (VAR) model for the first difference in the variables. It was found that the lag length is one.
- Cointegration equations: The Johansen cointegration test was conducted since all variables are integrated at the same order, I(1) and I(2). The results indicate that there are three cointegration equations at the 5% significance level, as the trace value surpasses the critical threshold. However, the maximum eigenvalue suggests that there are only two cointegration equations.
- Hypothesized tests: Table 8 below shows the results of various hypothesized tests. The “Trace” test statistic is used to determine the number of cointegrating equations. It indicates that there are three cointegrating equations at the 0.05 significance level.
- Interpretation: The presence of cointegrating equations suggests that there are long-term relationships among the variables, meaning that they move together in the long run. In this analysis, one can explore these cointegrating relationships further to understand how changes in one variable affect the others in the long term.
4.4.2. Results of Granger Causality Tests
- RGDPc → N2O (F-Stat. 4.647, p-value 0.0192, causality: yes):
- N2O → RGDPc (F-Stat. 3.099, p-value 0.0628, causality: no):
- ENGU → CO2 (F-Stat. 16.149, p-value 0.000, causality: yes):
- CO2 → ENGU (F-Stat. 11.583, p-value 0.0003, causality: yes):
- URP → RGDPc (F-Stat. 4.281, p-value 0.0252, causality: yes):
- RGDPc → URP (F-Stat. 4.363, p-value 0.0237, causality: yes):
- There is a statistically significant unidirectional causal relationship from real GDP per capita (RGDPc) to urban population (URP).
- URP → CO2 (F-Stat. 0.82434, p-value 0.4501, causality: no):
- CO2 → URP (F-Stat. 5.92209, p-value 0.0078, causality: yes):
5. Conclusions and Policy Implications
- Long-term cointegration: We have established that there is a substantial long-term cointegration among the variables, indicating a stable relationship between economic growth and air pollution in Oman.
- Positive link between economic growth and CO2 emissions: Our analysis has revealed a statistically significant positive association between economic growth and CO2 emissions, suggesting that, as the economy grows, so does the level of carbon dioxide emissions.
- N2O emissions correction: We found that there is an annual correction rate of approximately 14.9% in N2O emissions in the short term, indicating a dynamic adjustment towards an equilibrium.
- Causality analysis: The Granger causality analysis has shown unidirectional causal relationships between economic growth, energy use, urban population, and emissions in Oman, emphasizing the dynamic interplay between economic activity and air pollution.
- Balanced development: Oman should adopt a balanced approach to development that recognizes the interdependence of the environment and the economy. Policymakers should prioritize environmental preservation alongside economic growth to ensure long-term sustainability.
- Environmental protection initiatives: Stronger regulations, incentives for environmentally friendly innovation, and investments in renewable energy sources are crucial to reduce pollution levels and mitigate the negative impact of air pollution on economic growth.
- Policy framework: Oman needs a comprehensive policy framework that promotes sustainable practices and environmentally responsible policies. This framework should encourage the transition to cleaner energy sources and stricter environmental standards.
- Public engagement: It is essential to involve the general population in advancing environmental responsibility. Public awareness campaigns and education initiatives can play a significant role in fostering a culture of sustainability.
- Sectoral analysis: Delve into the contribution of various economic sectors in Oman to CO2 and N2O emissions. This entails breaking down economic and emissions data to pinpoint which sectors are primarily responsible for the observed relationships.
- Environmental impact assessment: Undertake a comprehensive assessment of the environmental impact, including ecological and health consequences, resulting from the documented air pollution in Oman. Such an evaluation can guide policymakers in setting priorities for environmental enhancements.
- Comparative Studies: Conduct comparative analyses involving other Gulf region countries to discern regional disparities in the interplay between economic growth and environmental variables. This approach can uncover best practices and lessons that are applicable to Oman.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Definition | Codes of Variable | Source |
---|---|---|---|
Dependent variable | Real GDP at constant 2011 national prices (Converted to the equivelant USD million, 2011) | RGDPc | PWT 10.0 * |
Independent variables | CO2 emissions (metric tons per capita) Nitrous oxide emissions (thousand metric tons of CO2 equivalents) | CO2 N2O | WDI, 2022 ** WDI, 2022 |
Control variables | Urban population (% of the total population) | URB | WDI, 2022 |
Energy use (kg of oil equivalents per capita) | ENGU | WDI, 2022 |
Variables | Mean | Median | Max. | Min. | Std. Dev. | CV |
---|---|---|---|---|---|---|
Ln RGDPC | 18,747.86 | 19,189.55 | 21,458.39 | 14,792.32 | 1740.012 | 9.281123286 |
Ln CO2 | 12.5148 | 13.0604 | 17.30974 | 6.566793 | 3.623088 | 28.95042669 |
Ln N2O | 703.4375 | 655 | 1090 | 340 | 265.1671 | 37.69590049 |
Ln URP | 75.26406 | 72.6835 | 87.044 | 66.102 | 5.698879 | 7.571846377 |
Ln ENGU | 126.1235 | 122.4913 | 181.8656 | 76.75578 | 39.29334 | 31.15465397 |
Variables | RGDPC | CO2 | N2O | URBN | ENGU |
---|---|---|---|---|---|
Ln RGDPC | 1 | ||||
Ln CO2 | 0.241857 | 1 | |||
Ln N2O | −0.096951 | 0.915519 | 1 | ||
Ln URP | −0.316633 | 0.762638 | 0.94191 | 1 | |
Ln ENGU | 0.056571 | 0.893628 | 0.89911 | 0.747322 | 1 |
Variable | Coefficient | Std. Error | t-Statistic | Pro. |
---|---|---|---|---|
Ln CO2 | 1164.263 | 145.1280 | 8.022319 | 0.0000 |
Ln N2O | −21.55100 | 1.961402 | −10.98754 | 0.0000 |
Ln URP | 236.2125 | 11.94890 | 19.76856 | 0.0000 |
Ln ENGU | 12.30533 | 12.52318 | 0.982604 | 0.3342 |
R-squared | 0.632001 | Mean dependent var. | 18,747.86 | |
Adjusted R-squared | 0.592572 | S.D. dependent var. | 1740.012 | |
S.E. of regression | 1110.651 | Akaike info criterion | 16.97975 | |
Sum squared resid. | 34,539,276 | Schwarz criterion | 17.16297 | |
Log likelihood | −267.6760 | Hannan–Quinn criter. | 17.04048 | |
Durbin–Watson stat. | 0.868178 |
Variable | Coefficient | Std. Error | t-Statistic | Pro. |
---|---|---|---|---|
Ln RGDPC | 0.149294 | 0.020395 | 7.320290 | 0.0000 |
Ln (RGDPcit)2 | −3.81 × 10−6 | 6.92 × 10−7 | 5.511561 | 0.0000 |
Ln CO2 | 27.15082 | 5.716932 | 4.749195 | 0.0001 |
Ln URP | 21.56324 | 2.447204 | 8.811379 | 0.0000 |
Ln ENGU | 1.490693 | 0.361947 | 4.118538 | 0.0003 |
R-squared | 0.986968 | Mean dependent var | 703.4375 | |
Durbin Watson (D.W). | 0.875121 | Akaike info criterion | 9.939011 | |
S.E. of regression | 32.43592 | Schwarz criterion | 10.16803 | |
Sum squared resid. | 28,406.40 | Hannan–Quinn criter. | 10.01492 | |
Log likelihood | −154.0242 |
Variables | Level Critical Values | First Difference Critical Values | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
1% | 5% | 10% | t-Values | p-Values | 1% | 5% | 10% | t-Values | p-Values | |
Ln CO2 | −3.662 | −2.96 | −2.612 | −2.6192 | 0.4791 | −3.670 | −2.964 | −2.621 | −4.88688 | 0.0004 * |
Ln N2O | −3.6617 | −2.960 | −2.6192 | 0.013780 | 0.9529 | −3.6702 | −2.9640 | −2.612 | −4.445660 | 0.0014 * |
Ln RGDPc | −3.6702 | −2.9640 | −2.621 | −0.902244 | 0.7735 | −3.6892 | −2.9719 | −2.6251 | −6.875273 | 0.0000 ** |
Ln URP | −3.6892 | −2.9719 | −2.6251 | 2.556510 | 1.0000 | −3.6892 | −2.972 | −2.6251 | −3.048164 | 0.0421 ** |
Ln ENRU | −3.6892 | −2.9719 | −2.6251 | −0.822161 | 0.7972 | −3.679 | −2.9678 | −2.6229 | −5.727279 | 0.0001 * |
Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|
0 | 23.02299 | NA | 6.60 × 10−5 | −1.112388 | −0.980428 | −1.066331 |
1 | 124.4002 | 180.2262 | 3.91 × 10−7 | −6.244458 | 5.716618 * | −6.060227 |
2 | 128.8578 | 7.181681 | 5.09 × 10−7 | −5.992102 | −5.068382 | −5.669699 |
3 | 145.3521 | 23.82506 * | 3.46 × 10−7 * | 6.408450 * | −5.088851 | −5.947875 * |
Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob. ** |
---|---|---|---|---|
None * | 0.657911 | 84.88720 | 69.81889 | 0.0020 |
At most 1 * | 0.512398 | 52.70670 | 47.85613 | 0.0163 |
At most 2 * | 0.409125 | 31.15902 | 29.79707 | 0.0346 |
At most 3 | 0.255103 | 15.37452 | 15.49471 | 0.0521 |
At most 4 * | 0.195855 | 6.539257 | 3.841466 | 0.0105 |
Error Correction: | D (N2O) | D (CO2) | D (RGDPC) | D (RGDPcit)2 | D (URP) | D (ENGU) |
---|---|---|---|---|---|---|
CointEq1 | −0.149294 | −0.008377 | −5.620052 | −215635.7 | 0.001124 | 0.096518 |
(0.020395) | (0.00216) | (2.11362) | (82262.9) | (0.00092) | (0.06386) | |
[−7.320290] | [−3.88557] | [−2.65897] | [−2.62130] | [1.21631] | [1.51144] |
Variables | F-Stat. | p-Value | Causality |
---|---|---|---|
RGDPc → N2O | 4.647 | 0.0192 | Yes |
N2O → RGDPc | 3.099 | 0.0628 | No |
ENGU → CO2 | 16.149 | 0.000 | Yes |
CO2 → ENGU | 11.583 | 0.0003 | Yes |
URP → RGDPc | 4.281 | 0.0252 | Yes |
RGDPc → URP | 4.363 | 0.0237 | Yes |
URP → CO2 | 0.82434 | 0.4501 | No |
CO2 → URP | 5.92209 | 0.0078 | Yes |
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Mohammed, M.; Abdel-Gadir, S. Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman. Sustainability 2023, 15, 16918. https://doi.org/10.3390/su152416918
Mohammed M, Abdel-Gadir S. Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman. Sustainability. 2023; 15(24):16918. https://doi.org/10.3390/su152416918
Chicago/Turabian StyleMohammed, Mwahib, and Sufian Abdel-Gadir. 2023. "Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman" Sustainability 15, no. 24: 16918. https://doi.org/10.3390/su152416918
APA StyleMohammed, M., & Abdel-Gadir, S. (2023). Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman. Sustainability, 15(24), 16918. https://doi.org/10.3390/su152416918