Reducing carbon dioxide emissions in Somalia: do renewable energy and urbanization matter?

Hassan, Ali Yusuf; Hussein, Omar Ahmedqani Article Reducing carbon dioxide emissions in Somalia: do renewable energy and urbanization matter? Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Hassan, Ali Yusuf; Hussein, Omar Ahmedqani (2024) : Reducing carbon dioxide emissions in Somalia: do renewable energy and urbanization matter?, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-12, https://doi.org/10.1080/23322039.2024.2409416 This Version is available at: https://hdl.handle.net/10419/321615 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Ali Yusuf Hassan & Omar Ahmedqani Hussein To cite this article: Ali Yusuf Hassan & Omar Ahmedqani Hussein (2024) Reducing carbon dioxide emissions in Somalia: do renewable energy and urbanization matter?, Cogent Economics & Finance, 12:1, 2409416, DOI: 10.1080/23322039.2024.2409416 To link to this article: https://doi.org/10.1080/23322039.2024.2409416 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 03 Oct 2024. Submit your article to this journal Article views: 845 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 ENVIRONMENTAL ECONOMICS & SUSTAINABILITY | RESEARCH ARTICLE Reducing carbon dioxide emissions in Somalia: do renewable energy and urbanization matter? Ali Yusuf Hassan and Omar Ahmedqani Hussein Department of Economics, SIMAD University, Mogadishu, Somalia ABSTRACT This study investigates the interplay between renewable energy Consumption, urbanization, and carbon emissions in Somalia, a region facing pressing environmental challenges. The study uses advanced econometric techniques like the ARDL model to uncover short- and long-term dynamics that impact environmental degradation. In the short term, this study found that renewable energy consumption and domestic investment have a significant negative correlation with carbon dioxide emissions, highlighting the immediate impact of environmental mitigation. Conversely, urban population dynamics have a modest immediate effect. In the long run, this study revealed that renewable energy consumption consistently correlates negatively with carbon emissions, indicating its potential to drive sustainable development and reduce reliance on carbon-intensive energy sources over time. However, the positive correlation between urban population and carbon emissions underscores the importance of addressing urbanization challenges to promote environmental conservation efforts. This study recommends policy interventions prioritizing renewable energy consumption and sustainable urban development to achieve long-lasting environmental sustainability in Somalia. IMPACT STATEMENT Somalia is one of the African counties suffer climate change problems without contributions, This study provides valuable empirical evidence on the relationship between renewable energy consumption, urbanization, and carbon dioxide emissions in Somalia, addressing a significant gap in existing research. By employing advanced econometric techniques, such as the ARDL model and Granger causality tests, the research confirms that renewable energy and domestic investment play a critical role in reducing CO2 emissions, while urbanization and economic growth contribute to increasing emissions. These findings highlight the need for Somalia to prioritize investments in renewable energy infrastructure and sustainable urban planning to mitigate the adverse effects of carbon emissions. Additionally, the study’s recommendations for policy interventions, including promoting cleaner energy sources, enhancing domestic investment, and raising environmental awareness, offer actionable insights that can drive Somalia toward achieving environmental sustainability and meeting its climate change goals. ARTICLE HISTORY Received 19 March 2024 Revised 25 July 2024 Accepted 22 September 2024 KEYWORDS Carbon dioxide; renewable energy; urbanization; ARDL; Somalia SUBJECTS Environmental Economics; Environment & the Developing World; Environmental Change & Pollution; Urban Development 1. Introduction Climate change is one of the most pressing global challenges in recent decades. The continent witnesses over 770,000 annual deaths due to air pollution alone, and airborne pollution has triggered disabilities and reduced overall life expectancy for more than 600,000 citizens in the sub-Saharan region (Amegah & Agyei-Mensah, 2017; Gyamfi et al., 2021). The accumulation of CO2 and other greenhouse gases in the atmosphere traps heat, leading to an increase in global temperatures. According to the Intergovernmental Panel on Climate Change (IPCC, 2014), the average global temperature has risen by approximately 1C above pre-industrial levels due to human activities, primarily the burning of fossil fuels. This warming has CONTACT Ali Yusuf Hassan [email protected] Department of Economics, SIMAD University, Mogadishu,Somalia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2409416 https://doi.org/10.1080/23322039.2024.2409416 resulted in more frequent and severe weather events, including heatwaves, droughts, heavy precipitation, and tropical cyclones, which pose substantial risks to ecosystems and human societies (Jeffry et al., 2021; Lee et al., 2023). Increasing population growth with energy constraints drive humans to actively tap into natural reserves of coal, fossil fuel, and natural gas, resulting in the emission of greenhouse gases, predominantly Carbon dioxide (Adams & Acheampong, 2019; Ideris et al., 2021; Martins et al., 2021). In developing countries, the growing population and the predominant use of fossil fuels as the primary energy source have resulted in increased carbon dioxide emissions with detrimental environmental consequences (Adams & Nsiah, 2019;Warsame&Abdi,2023). Mitigating CO2 emissions is thus a critical objective for nations worldwide. Renewable energy sources, including biomass, hydropower, geothermal, wind, and solar energy, are the most affordable methods for eliminating fossil fuels, which have severe environmental impacts (Olabi & Abdelkareem, 2022; Sayed et al., 2021). Renewable energy also has the potential to achieve economic, social, and environmental objectives simultaneously, as formulated in the sustainable development goals in Africa (Musah et al., 2023; Schwerhoff & Sy, 2017). Somalia is dealing with energy constraints, primarily relying on burning charcoal and using thermal sources, resulting in a substantial release of greenhouse gases (Somalia, 2018). With a low carbon footprint compared to the world, it has hit the worst effects of climate change, experiencing five consecutive failed rainy seasons, making over 8 million people require immediate humanitarian assistance (Office for the Coordination of Humanitarian Affairs, 2023). In addition to governance challenges, droughts and floods make it highly vulnerable to climatic variations (Warsame & Sarkodie, 2022; Wheeler, 2011). This raised concerns for the cleaner environment of sustainable development goals, underscoring the urgent need to lower carbon emissions. The increasing concern revolves around the impact of the city population and disproportionate climate change effects, like frequent droughts and floods, on the environmental health of unstable countries such as Somalia. This study aims to research the effects of renewable energy usage and urbanization on carbon dioxide emissions in Somalia, an area with limited existing research. Different from previous studies, we will use an autoregressive distributed lag model to classify the short and long-term effects of estimated variables of this study. Additionally, the paper will employ cointegration estimators like FMOLS to understand better the long-term impact of renewable energy usage and urbanization on environmental degradation in Somalia. The study will also utilize the Granger causality test to predict the direct causality between the variables. Various studies have examined the impact of renewable energy on carbon emissions. For example, Dogan and Seker (2016) investigated the effects of renewable on carbon emissions in leading renewable energy-producing countries. Their research showed that renewable energy helps reduce carbon emissions both in the short- and long term. Similarly, Adams and Acheampong (2019) Studied the role of renewable energy in reducing CO2 emissions in 46 sub-Saharan African nations from 1980 to 2015. They found that renewable energy diminishes carbon emissions. Similarly, Saidi and Omri (2020) Used fully modified ordinary least squares (FMOLS) and vector error correction models (VECM) to estimate the impact of renewable energy on carbon emissions in 15 major renewable energy-consuming countries. Their research demonstrated that renewable energy efficiency results in lower carbon dioxide emissions. Similarly, Ridzuan et al. (2020) studied the effect of renewable energy on carbon emissions in Malaysia from 1978 to 2016. They confirmed that adopting renewable energy sources mitigates CO2 emissions and that renewable energy has benefits in reducing greenhouse gas emissions. Al-Mulali et al. (2015) delved into the determinants of pollution in Europe. They utilized comprehensive econometric analyses to examine how renewable energy influences environmental degradation. Their study highlighted the positive impact of renewable energy in mitigating CO2 emissions in Europe, reinforcing the necessity of transitioning towards cleaner energy sources. Musah et al. (2024) also presented a comparative impact of renewable energy consumption on CO2 emissions in Sub-Saharan African countries. They revealed that renewable energy consumption is essential to reduce CO2 emissions, underscoring its crucial role in achieving sustainable environmental outcomes in Sub-Saharan African countries. Whether the increased use of renewable energy sources effectively mitigates CO2 emissions, as confirmed by Adams and Nsiah (2019), who investigated the impact of renewable energy on reducing carbon dioxide (CO2) emissions in 107 countries. Through rigorous analysis, they demonstrated that renewable energy significantly contributes to reducing CO2 emissions. On the other hand, Warsame et al. (2023) found a negative long-term relationship between renewable energy consumption and carbon dioxide emissions in Somalia, 2 A.Y. HASSAN AND O.A. HUSSEIN highlighting the importance of improving access to clean energy to mitigate the gradual rise of carbon dioxide emissions. In contrast, Warsame and Sarkodie (2022) revealed conversely that energy consumption positively affects CO2 emissions in Somalia. The migration trend to cities has been increasing and is expected to continue in the coming years. According to Moriarty and Honnery (2015), Urbanized cities are responsible for a 70% increase in greenhouse gas emissions. In some countries, income is a significant factor in urbanization. Akram et al. (2020) discovered that the impact of urbanization on carbon emissions follows an inverted U- shape at different levels of CO2 emissions. This indicates that carbon emissions increase more in the mid-range of urbanization rather than at the extremes, which can aid in reducing overall emissions. Furthermore, (Liu et al., 2022) demonstrated that urbanization significantly contributes to higher carbon emissions in China, highlighting the main role of urbanized cities in affecting carbon emissions. Wang et al. (2021) investigated the relationship between urbanization and carbon emissions in OECD countries. Their findings indicated that urbanization initially leads to increased carbon emissions. This indicates that urbanized cities may adopt more economic practices and industries as they develop, which increases the overall environmental footprint. Similarly, Mignamissi and Djeufack (2022)examined the relationship between urbanization and CO2 emissions in 48 African countries from 1980 to 2016. Their study, which utilized an augmented STIRPAT model, indicated urbanization’s positive and significant overall effect on global warming. However, Fang and Yang (2020)analyzedtheimpactof urbanization and urban income on carbon dioxide emissions in China and found that the relationship may not be significant due to effective urban planning, technological advancements, policy interventions, behavioral changes, economic shifts, and regional disparities. These factors collectively contribute to a complex interplay that can diminish the impact of urbanization on carbon emissions. Sufyanullah et al. (2022) studied whether urbanization influences carbon dioxide emissions. Employing the ARDL bound testing approach. Their findings shed light on the significant positive impact of urbanization on carbon emissions, providing valuable insights into the environmental implications of rapid urban development. In Somalia there are contradicted results about how urbanization affects CO2 emissions. Some studies, like Warsame (2022), revealed that urbanization negatively affects carbon emissions, while others, like Warsame et al. (2023) indicated that rapid urbanization contributes to increased CO2 emissions in Somalia. Although many studies have been done on the relationship between urbanization, renewable energy, and carbon emissions, very few comprehensive studies have integrated the effects of urbanization and the adoption of renewable energy on carbon emissions, especially in poor countries like Somalia. Existing studies on Somalia investigate the effects of renewable energy and urbanization on carbon emissions separately and provide contradictory results, which emphasize the necessity for more in-depth and context-specific research. Therefore, this study fills this gap by investigating the combined effect of renewable energy and urbanization on carbon emissions. This study aims to provide empirical evidence on the impact of renewable energy usage and urbanization on environmental degradation in Somalia. The rest of the study is arranged as follows: Section 2 presents the methodology and the data description; Section 3 provides the empirical results and their discussion; and Section 4 summarizes the study results and provides policy recommendations. 2. Methodology 2.1. Data This study uses annual time series data from 1990 to 2020 in Somalia to investigate how urbanization and renewable energy contribute to CO2 emissions. Carbon dioxide emission is the dependent variable of this study, whereas renewable energy and urbanization are the explanatory variables. The study also uses economic growth and domestic investment as control variables to determine the study’s robustness. The urbanization, carbon dioxide emissions, and capital formation data were obtained from the World Bank, whereas the economic growth and renewable energy variables were sourced from the OICSESRIC database (see Table 1 for details). COGENT ECONOMICS & FINANCE 3 2.2. Econometric modelling This study explores the relationship between renewable energy, urbanization, and CO2 emissions in Somalia. According to Wheeler (2011), Somalia is recognized as one of the most climate-vulnerable countries globally, and it faces significant challenges due to climate change. The analysis of this study highlights the severe impact of Somalia’s environmental quality and climate change, emphasizing the urgent need for effective mitigation and adaptation strategies to address these issues. This study employs a comprehensive empirical approach to investigate the impact of renewable energy consumption and urbanization on carbon dioxide emissions in Somalia. The first step involves using unit root tests to assess the stationarity of the variables over time, a crucial process for determining the appropriate modeling techniques and ensuring the reliability of the time series analysis. This study uses the Augmented Dickey-Fuller (ADF) and Phillips-Perron unit root tests. Following the unit root tests, the study performs a cointegration analysis using the F-bound cointegration test to determine whether a lasting relationship exists among the variables. This step helps identify long-term equilibrium relationships and informs the choice of subsequent modeling techniques. Next, the study utilizes Autoregressive Distributed Lag (ARDL) models to explore the short- and long-term correlations among the variables. These analytical frameworks allow for the examination of dynamic interactions and provide valuable insights into how urbanization and the integration of renewable energy impact carbon emissions over time. Additionally, the study employs various techniques such as Canonical Cointegrating Regression (CCR), Dynamic Ordinary Least Squares (DOLS), and Fixed Effects Fully Modified Ordinary Least Squares (FMOLS) to explore the long-term relationships between the variables in greater detail. These methods offer robust estimators and deeper insights into the relationships under study. Finally, the research uses paired Granger causality tests to ascertain the direction of causality among the variables, with a primary focus on the impact of economic growth, urbanization, renewable energy consumption, and domestic investment on carbon dioxide emissions. This multi-faceted approach provides a thorough understanding of the factors influencing CO2 emissions in Somalia and informs policy recommendations for mitigating environmental degradation. The model of how renewable energy and urbanization reduce carbon dioxide in Somalia can be specified as follows: CO2t¼fðREt,UPt,DIt,EGtÞ(1) where CO2 is carbon dioxide emission, RE is the amount of renewable energy consumed, UP is the urban population, EG is economic growth, and DI is the domestic investment. Carbon dioxide emissions are a function of urban population, use of renewable energy, domestic investment, and economic growth. Below is the specification of the econometric model used in this study: CO2t¼d0þd1REtþd2UPtþd3DItþd4EGtþet(2) where t is the time series data, the coefficients d 1 ,d 2 ,d 3 , and d 4 predict the change in CO2 t for a 1-unit increase in each independent variable, holding the other variables constant. When all independent variables are zero, the intercept term d 0 indicates the expected value of CO2 t , and e t indicates the error term that accounts for fluctuations in CO2 t that cannot be explained. This study utilizes the ARDL bound test to explore the cointegration among the estimated variables in this study related to environmental degradation in Somalia. The ARDL method offers researchers a flexible and powerful tool for analyzing cointegration among variables in economic and social research (Hassan & Mohamed, 2024; Warsame et al., 2023). One of its key advantages lies in its ability to handle variables with different orders of integration, including those that are stationary [I (0)], non-stationary [I (1)], or a combination of both (Warsame et al., 2022). This flexibility eliminates the need for prior transformations of variables to achieve stationarity, making the ARDL approach particularly attractive for empirical studies where variables may exhibit diverse behavior patterns. ARDL simplifies the modeling procedure and reduces the risk of biased Table 1. Variables description. Variable Code Measurement Source Carbon dioxide emission CO2 Carbon emission (kt) World Bank Renewable energy RE Renewable energy consumption SESRIC Urbanization UP Urban Population World Bank Domestic investment DI Gross fixed capital formation (current US$) World Bank Economic growth EG GDP, Constant 2015 Prices SESRIC 4 A.Y. HASSAN AND O.A. HUSSEIN estimation due to incorrect specifications. This streamlining of the analysis enhances the efficiency and reliability of research findings, contributing to more robust empirical studies. DCO2t¼d0þd1DCO2t−1þd2DREt−1þd3DUPt−1þd4DDIt−1þd5DEGt−1þX p i¼1 a1DCO2t−1þX p i¼1 a2DREt−1 þX p i¼1 a3DUPt−1þX p i¼1 a4DDIt−1þX p i¼1 a5DGDP þlECTt−1 (3) where Dthe first deference operator, qindicates the optimal leg length of the estimated variables, d 1 , d 2 ,d 3 ,d 4 , and d 5 , are the coefficient parameters of long-run relationships, a 1 ,a 2 ,a 3 ,a 4 , and a 5 quantify the short-run effects of the explanatory variables on changes in CO2 emissions. Additionally, the model includes an error correction term (ECT t-1 ) capturing any deviations from the long-term equilibrium relationship among the variables, reflecting the speed of adjustment of CO2 emissions to changes in the explanatory variables. 3. Empirical results and discussions 3.1. Descriptive statistics Table 2 presents a comprehensive overview of the dataset’s descriptive statistics and correlation coefficients, offering valuable insights into the variables involved. The descriptive statistics reveal key characteristics of each variable. The mean for carbon dioxide emissions (CO2) is 612.68, with a standard deviation of 59.21, indicating moderate variability around the average. Renewable energy consumption (RE) has a mean of 92.65 and a standard deviation of 2.26, suggesting stable consumption patterns. The urban population (UP) shows a mean of 4.06 with a low standard deviation of 1.73, indicating some variability. Domestic investment (DI) has a mean of 461.33 and a standard deviation of 215.09, while economic growth (EG) has a mean of 3437.73 with a standard deviation of 1666.26. Skewness and kurtosis values provide further insights into the data’s distribution. CO2 and RE show slight left-skewed, while UP, DI, and EG display right-skewed distributions. The Jarque-Bera test results indicate that most variables follow a normal distribution, except renewable energy and domestic investment, which show significant deviation. Additionally, the correlation coefficients in Table 2 shed light on the relationships between the variables. Renewable energy consumption is inverse to CO2 emissions, suggesting that higher renewable energy consumption is associated with lower carbon dioxide emissions. Urban population growth, domestic investment, and economic growth positively correlate with CO2 emissions, indicating that urban population growth, domestic investment, and economic growth are associated with higher CO2 emissions. Also, strong positive relationships among the urban population, domestic investment, and economic growth reflect the interconnectedness between urbanization, economic activity, and domestic investment. Table 2. Descriptive statistics. Descriptive information CO2 RE UP DI EG Mean 612.683 92.652 4.055 461.331 3437.728 Median 625.700 93.280 3.801 376.819 3025.988 Maximum 735.120 95.520 7.630 972.141 6638.503 Minimum 486.600 86.290 1.952 252.440 1526.152 Std. Dev. 59.212 2.260 1.731 215.087 1666.264 Skewness −0.384 −1.232 0.542 1.174 0.606 Kurtosis 2.797 3.882 2.104 3.229 2.073 Jarque-Bera 0.816 8.848 2.554 7.191 3.008 Probability 0.665 0.012 0.279 0.027 0.222 Correlation CO2 1.000 RE −0.243 1.000 UP 0.316 0.819 1.000 DI 0.507 0.594 0.933 1.000 EG 0.403 0.758 0.990 0.957 1.000 COGENT ECONOMICS & FINANCE 5 3.2. Unit root test analysis Table 3 presents the results of the Augmented Dickey-Fuller (ADF) unit root test and the Phillips-Perron (PP) test for assessing the stationarity of the variables at both level and first difference. The table provides test statistics for each variable with both a constant and a constant plus trend specification. The ADF and PP tests show that most variables are not stationary at the level, except renewable energy consumption, which is stationary at a 1% significant level. At first differencing, all variables become stationary, as the significant test statistics indicate in the ADF and PP tests. These results, with their direct implications for our study, indicate that –while the original series of most variables are non-stationary, their first differences are stationary –it is not only academically significant but also crucial for practical applications. Hence, it informs that the ARDL is the appropriate model since it can handle variables integrated into different orders. 3.3. Bound test and ARDL outcomes This study used the F-bound cointegration test to determine if long-term relationships exist among CO2 emissions, renewable energy, urban populations, domestic investment, and economic growth. The null hypothesis assumes the absence of cointegration, indicating no long-term relationships among the examined variables in this study. The results presented in Table 4 demonstrated that stable long-term relationships exist among the variables since the calculated F-statistic is 6.053, exceeding the critical value of 5.06 at the significance level of 1%. After confirmation of the long-term association among the estimated variables, The ARDL results in Table 5 present the short- and long-term results of the ARDL model. The short-term results indicated that renewable energy consumption and domestic investment have a negative effect on CO2 emissions, meaning that a 1-unit increase in renewable energy consumption or domestic investment decreases CO2 emissions on average by 49.8 and 0.40 units, respectively, with a significance level of 1%. In contrast, economic growth has a significant positive relationship with CO2 emissions in Somalia, indicating that a 1 unit increase in Somalia’s GDP increases CO2 emissions on average by 0.03 unit in the short term, with a significance level of 5%. In the long-term results, economic growth and urban population (UP) have a positive association with carbon dioxide in the long term at a 5% significant level. Renewable energy and domestic investment negatively correlate with CO2 at a 1% significant level in Table 4. F bound cointegration test. F-Bounds test Null hypothesis: no levels of relationship Test Statistic Value Significant I (0) I (1) F-statistic 6.053 1% 3.74 5.06 K 4 5% 2.86 4.01 10% 2.45 3.52 Table 3. ADF unit root test. At Level ADF PP Variables With Constant With Constant & Trend With Constant With Constant & Trend CO2 −2.026 −2.799 −2.164 −2.807 RE −5.586 −3.317−5.586 −4.727 UP 5.463 −0.835 10.745 −0.704 DI 2.575 −1.149 2.357 −1.091 EG 1.811 −2.192 1.957 −2.924 At Fist Difference d(CO2) −3.261 −3.609 −3.173 −3.584 d(RE) −3.522 −3.463−3.522 −3.463 d(UP) −3.135 −5.335 −3.009 −10.077 d(DI) −3.785 −4.775 −3.799 −5.996 d(EG) −4.228 −4.671 −4.222 −5.275 Notes: ,,Indicate the significance level at 1%, 5%, and 10% respectively. d donates the first difference operator. 6 A.Y. HASSAN AND O.A. HUSSEIN the long term. Additionally, the error correction term (ECM) is negative and significant at the 1% level, indicating a speed of adjustment of 45.2% back to equilibrium in each period. We also conducted diagnostic checks in Table 5 to assess the model’s validity, normality, serial correlation, homoscedasticity, and stability. The normality, autocorrelation, Ramsey RESET, and heteroscedasticity tests all show high probabilities, indicating no issues with normality, autocorrelation, heteroscedasticity, or misspecification. The diagnostic tests suggest that the model is well-specified and robust. The adjusted R-squared value of 0.871 indicates that the model explains approximately 87.1% of the variation in CO2 emissions in Somalia, demonstrating a strong fit. After analyzing the ARDL model, this study employed FMOLS, DOLS, and CCR estimators to confirm the robustness of ARDL results. The results in Table 6 revealed that all cointegration estimators have similar result signs but slightly different magnitudes and standard errors. For example, Renewable energy and domestic investment have significantly negative relations to carbon dioxide emissions in the long run. In contrast, urban populations and economic growth significantly enhance CO2 emissions in the long run. Moreover, the models exhibit high explanatory power - renewable energy, domestic investment, urban populations, and economic growth - with R-squared values of 0.952 for FMOLS, 0.996 for DOLS, and 0.951 for CCR. Therefore, FMOLS, DOLS, and CCR estimations provided a robust confirmation analysis of the ARDL results. The results obtained from the Cointegrations models in this study have confirmed the accuracy of the ARDL results. 3.4. Granger causality test This study employs pairwise Granger causality tests to determine how the estimated variables –renewable energy consumption, urban population, domestic investment, and economic growth –cause each other. The null hypothesis in each test is that the first variable does not cause the second variable. The results presented in Table 7 demonstrated that there is unidirectional causality from renewable energy, urban population, domestic investment, and economic growth to carbon dioxide emissions, as indicated by p-values below 0.05 or 0.10. In other words, the variables renewable energy, urban population, domestic investment, and economic growth significantly influence CO2 emissions. This implies that Table 6. Results of FMOLS, DOLS, and CCR Estimators. Variable FMOLS DOLS CCR Coefficient Std. Error Coefficient Std. Error Coefficient Std. Error RE −52.896 2.204 −68.762 3.279 −54.688 3.489 UP 0.006 0.0016 0.009 0.0024 0.008 0.0021 DI −0.385 0.031 −0.464 0.029 −0.416 0.067 EG 0.054 0.008 0.047 0.008 0.062 0.018 C 5261.338 194.203 6680.286 295.793 5417.065 305.441 R2 0.952 0.996 0.951 Notes: , Indicate the significance level at 1% and 5% respectively. Table 5. ARDL results. Variable Coefficient Std. error D(RE) −49.797 7.725 D(DI) −0.399 0.072 D(EG) 0.030 0.013 RE −55.220 7.611 UP 68.683 31.201 DI −0.446 0.091 EG 0.067 0.027 ECM (-1) −0.452 0.075 Diagnostic check Value Probability Normality test 0.143 0.931 LM test 0.584 0.747 Heteroscedasticity test 9.190 0.327 Ramsey RESET test 0.006 0.939 R 2 0.889 Notes: , Indicate the significance level at 1% and 5% respectively. D represents the short-run coefficients. COGENT ECONOMICS & FINANCE 7