Natural resource rents and sustainable development in MENA Economies: An empirical study

Natural resource rents and sustainable development in MENA Economies: An empirical study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Natural resource rents and sustainable development in MENA Economies: An empirical study Omar Ahmed Abdulraqeb, Cao Erbao, Abdullah Aloqab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4986566/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change and its effects around the globe are demanding sustainable development, which encompasses socio-economic development along with taking into account the environmental aspect. The Middle East and North African economies (MENA) possess substantial reserves of natural resources. These natural resources played an important role in the development of this region, and studies are scarce in this regard for this region. Therefore, this study investigates the impact of natural resource rents on sustainable development in MENA. This study used the Sustainable Development Index, which incorporates socio-economic and environmental aspects. Thus, this study is different from other studies in this regard in the MENA region. Additionally, this study also considered other vital factors of sustainable development highlighted in the literature. These factors are renewable energy (REC), non-renewable energy (NREC), and trade openness (TRADE). This is a panel study; thus, a cross-sectional dependence test is carried out, which indicates that second-generation unit root tests have to be used for unit root problems. Likewise, the Westerlund cointegration test is used for examining panel cointegration between variables. Panel autoregressive distributed lagged (PARDL) technique is carried out for long- and short-run effects of explanatory variables on sustainable development. The results indicate that natural resource rents (TNR) have a negative impact on sustainable development, while REC, NREC, and TRADE positively influence sustainable development in the long run. sustainable development renewable and non-renewable energy natural resource rents trade openness 1. Introduction Sustainable development is one of the feasible solutions to environmental issues. It is defined as employing society's resources in such a way that future societies do not go without. The World Commission on Environment and Development's "Our Common Future" study roughly defines sustainable development as: in order for future societies to meet their requirements, we must reduce the environmental repercussions of current consumption. In this context, sustainable development consists of three components: social, environmental, and economic. Meanwhile, energy use affects the economy, society, and the environment. However, nonrenewable energy use is damaging to our planet. Such are conventional sources of energy that utilize fossil fuels, that have a favorable impact on GDP but have produced environmental contamination through waste generated by fossil fuels. Almost all countries use fossil fuels to fulfill their need for energy (Elum & Momodu, 2017 ). According to the World Health Organization ( 2016 ), air pollution is one of the most significant risk factors for human health all around the world, alongside hypertension and diabetes. To minimize environmental degradation, it is better to use REC than NREC. Using REC can help meet the growing energy needs while also achieving sustainable development (Ozturk & Acaravci, 2011 ). In addition, according to projections from scientific studies, the total supply of power in 2100 may consist of 30–65% REC, which would lead to a substantial reduction in carbon emissions (Van et al., 2017). The energy sector represents the largest portion of the world economy (Vezzoli et al., 2015 ). Also, energy is a key contributor to economic growth (GDP), which is connected to all aspects of development, lowering poverty, and raising the standard of living (Bilgili et al., 2017 ; Kaygusuz & Bilgen, 2009 ; Ockwell, 2008 ; Yahya & Rafiq, 2019 ). In other words, modern society satisfies its own demands; it is depleting future generations' access to a source of production that is getting more and more polluted. Hence, energy serves as a link between the current and next generations. Therefore, the preferences for energy of the current generation are influencing the environment as well as consumption of goods and services by future generations. As a result, sustainable development and energy consumption are closely related. Most countries from the Middle East and North Africa (MENA) have larger oil and natural gas reserves, and they heavily rely on oil rents. In MENA countries during the period 2000–2019 were 22.22 percent, 16.1 percent, 23.19 percent, 48.24 percent, 46.42 percent, 48.74 percent, 35.87 percent, 25.86 percent, 38.39%, and 21.14 percent of Gross Domestic Product (GDP). Furthermore, MENA economies' average TNR during 2000–2019 are lower than 6% (World Bank, 2022). Timmerberg et al. ( 2019 ) found that the majority of MENA economies have a miserable proportion of REC in their overall energy mix. Keeping in view the energy and environmental degradation situation in MENA, for example, Arouri et al. ( 2012 ) conducted research in MENA economies to analyze the effect of energy use and per capita income on carbon emissions. Alkasasbeh et al. ( 2023 ) examined the effects of income and energy use on carbon emissions in the Middle East. Gorus and Aslan ( 2019 ) conducted research to determine the economic factors that contribute to the degradation of the environment in the MENA countries. Mahmood et al. ( 2023 ) studied the effect of oil and natural gas on carbon emissions in MENA economies. None of the studies in the literature determined the impact of TNR on sustainable development, as most of the research studies used CO2 as a proxy for environmental degradation or environmental sustainability, for instance, Bilgili et al. ( 2023 ), whereas this novel study investigates the impact of TNR on sustainable development in the MENA region along with other important factors of sustainable development. This study proxy sustainable development with sustainable development index (SDI) which is the ecological efficiency of the human development index. SDI is gauged through the ratio of human development index to ecological overshoot in a country. 2. Literature Review Several studies have confirmed that TNR reduces environmental degradation, including those of emerging and developed nations (Altinoz & Dogan, 2021 ; Bekun et al., 2019 ; Umar et al., 2020 ; Khan et al., 2021 ). Ulucak and Ozcan ( 2020 ) found a one-way causal association going from total rents from natural resources to environmental degradation in the OECD countries. Ullah et al. ( 2021 ) studied the world’s top REC using countries and concluded that natural rent causes an increase in emissions of carbon leads to degradation of environment. Yu-Ke (2021) found that minerals, oil, and forest rent contribute significantly to environmental degradation in G-20 countries. Shen et al. ( 2021 ) got a positive linkage among rents from natural resources and environmental sustainability in China. Zuo et al. ( 2021 ) taken EFP and check the impact of rents from natural resources on EFP for 90 countries involved in the Belt and Road Initiative. The study covered time period from 1991–2018 and concluded that total rents from natural resources to the environmental quality. The empirical results found the positive impact of REC on environmental degradation in case of Chile (Kirikkaleli et al., 2022 ). Ben et al. (2016) found one directional causality from renewable energy use to environmental degradation in North African countries. Bilgili et al. ( 2017 ) utilized panel data analysis to demonstrate a negative correlation among renewable energy and environmental degradation across organizations for economic cooperation and development countries. In case of Turkey the REC declines emissions of greenhouse gases (Bölük & Mert, 2015 ). The countries with high law and accountability have negative association between renewable energy use and environmental degradation (Szetela, 2022) while the studied by Ullah et al., ( 2021 ) concluded that REC use has hinger the ecological footprint in case of top REC consumer countries of the World. The study found for fastest developing economies where Paramati et al. ( 2017 ) investigated the impact of REC use on GDP along with the environmental quality and they found the favorable impact of REC use on GDP as well as on environmental quality. Prior researchers studied the association among REC use on environmental quality as well as on economic growth. Güney ( 2019 ) toke developed and developing countries and studies that sustainable development is affected by REC and REC use. The study selected 73 developing countries from the world and 40 developed economies and found positive association among renewable, NREC use and sustainable development in case of developed and developing countries. In case of European Union economies, Bekun et al. ( 2019 ) studied the causal association between economic growth, nonrenewable energy, and environmental quality. The study found the positive impact of NREC use and income of the country on environmental degradation. Opeyemi (2019) investigated the association among NREC and REC use and carbon emissions for Nigeria taking the period 1987–2016. The conclusions indicated that the environment is degrading by non-renewable energy use. Previous studies examined the consequence of trade openness on sustainable development in which Cole and Elliott ( 2003 ) examined the trade and environment composition effect. Omri ( 2013 ) investigated the impact of CO2 on TR and other variables for MENA economies. This study used simultaneous equation model. The results indicated that there is an inverse association between trade and environmental degradation. Shahbaz et al. ( 2017 ) explored the association between TR and environmental degradation by incorporating GDP as potential variable in study for three groups of 105 countries. The study grouped the sample into countries with higher, middle, and lower incomes for the period 1980 to 2014. The outcomes concluded the inverted U-shaped association between trade and quality of environment for all samples. Shahbaz et al. ( 2019 ) took data for 105 developed and developing countries to investigate how trade affects the quality of environmental. They used Padroni and Wester Lund test for checking the long run impact. The results indicated TR hindered the quality of environment. Hasanov et al. ( 2017 ) investigated the effect of trade openness on environmental degradation in case of oil exporting countries and found the insignificant impact of trade on environmental degradation. Ling et al. ( 2015 ) concluded that TR improve environmental quality in case of Malysia. Mahmood et al. ( 2019 ) explored the effect of trade on environmental quality for Tunisia. The study used ARDL approach and selected time period 1971–2014. The results indicated that there is positive and insignificant effect of TR on CO2. 3. Methodology Using a panel data set for Middle East and North African economies covering time from 2000 and 2022. This study analyzes panel data for Algeria, Egypt, Iran, Iraq, Kuwait, Morrocco, Oman, Qatar, Saudi Arabia, Turkey, and United Arab Emirates. The taken variables for the study are sustainable development index, TNR, REC, NREC use and TR. Sustainable development index is used for sustainable development which is measure through the ratio of human development index over ecological overshoot. Renewable energy use is taken as the combination of hydro energy, wind energy and nuclear energy consumption. NREC is taken as the combination of oil energy, gas energy and coal energy consumption. This study used sustainable development as dependent variable and the others are used as independent variables. The TNR, REC and NREC are treated as main variables. Most of the researchers use TR as control variable (Xiaoman et al., 2021 ; Khalid et al., 2022 ), so this study uses TR as control variable. These variables are taken from the World Development Indicators (WDI) databank (World Bank, 2023) and British Petroleum (BP) online database (BP, 2023). A detailed overview of these variables can be found in Table 1 . Prior to performing unit root testing, cross-sectional dependence (CD) is evaluated. CD results from variables such as economic integration and residual interdependence. The presence of CD is determined with the Pesaran CD test. Addressing CD is critical since it affects the consistency of unit-root and the cointegration results. Following the CD evaluation, the study uses unit root tests using panel data. A first-generation technique does not address CD issues in datasets due to its shortcomings (Im et al., 2003). For this reason, it is crucial to ensure that data are stationary. As a Table 1 Description of the variables Variables Symbol Description Sustainable Development Index SDI Measured in sustainable development Index Total Natural Resources Rent TNR Total natural resources rents (measured in % of GDP) Renewable Energy Consumption REC Combination of Hydro Energy, Wind Energy and Nuclear energy and measured as Exajoule Trade Openness TR Trade (% to GDP) Non-Renewable Energy Consumption NREC Combination of Oil Energy, Gas Energy and coal energy and measured as Exajoule result of CD difficulties, the second-generation unit root test for panel data are CADF and CIPS presented by (Pesaran, 2007). This method is expected to provide dependable and consistent stationarity features. Panel cointegration of underlying variables is used to forecast long-term values. Traditional cointegration approaches may produce incorrect findings in the presence of CD and heteroscedasticity. The present study uses Westerlund and Edgerton's (2008) panel cointegration test to analyze CD, autocorrelation, and structural breakdowns. The Panel ARDL model presented by Pesaran and Smith ( 1995 ) and Pesaran et al. ( 1999 ) used to estimate long-run estimates when variables are cointegrated over the long run. Moreover, the cointegration process does not lead to simultaneity biases in these estimates. Also, the long-run estimates are not affected by autocorrelation. Thus, this study developed the following mode: $$\:SDI=f(TNR,\:REC,\:NREC,TR)$$ The empirical model can be expressed as follows after considering panel settings: $$\:{SDI}_{it}=\:{\beta\:}_{0i}+{\beta\:}_{1i}{TNR}_{it}+{\beta\:}_{2i}{REC}_{it}+{\beta\:}_{3i}{NREC}_{it}+{\beta\:}_{4i}{TR}_{it}+\:{\in\:}_{it}$$ where i represents the cross-section units (i = 1, 2, 3,. .. N), t represents the time period (t = 1, 2, 3, ... T) and \(\:{\beta\:}_{0}\:\) shows intercept, \(\:{\beta\:}_{1},{\beta\:}_{2},{\beta\:}_{3},{\beta\:}_{4}\) shows the slopes and \(\:\in\:\:\) represents error term. In addition, SDI stands for Sustainable Development Initiative, TNR for Total Natural Resource Rent, REC for Renewable Energy Use, NREC for Non-Renewable Energy Use, and TR for Trade Openness. The study covers twelve MENA countries. 4. Results and Discussion Table 2 exhibits the descriptive statistics of the variables. The sustainable development index (SDI) is measured as an index number which shows the means value of SDI is 0.51. the maximum SDI is 0.79 and minimum values is 0.10. TNR is gaged as percentage in GDP. The mean value of TNR is 23.23%. The maximum % of TNR is GDP is 65.32% and minimum values is 1.79%. The renewable energy consumption and nonrenewable energy consumption are taken in exajoule. The mean values of REC and NREC are 0.08 and 3.06 exajoule respectively while the maximum value of REC is 0.14 and NREC is 12.01 and minimum value of REC is 0.04 and NREC is 0.40 exajoule. TR is gaged as percentage of GDP. The mean value of TR is 76.40% and the maximum Trade value is 172.80 and minimum value of Trade is 29.87%. The standard deviation shows the fluctuation in data which shows the Trade has more variation than other variables. Table 2 Descriptive statistics SDI TNR REC NREC TR Mean 0.51 23.23 0.08 3.06 76.40 Maximum 0.79 65.32 0.14 12.01 172.80 Median 0.64 23.97 0.07 1.88 70.73 Minimum 0.10 1.79 0.04 0.40 29.87 Std. Dev. 0.24 17.70 0.02 2.94 28.87 The CD test results are revealed in Table 3 , which confirm the presence of the CD in our data set. The CD test's alternative hypothesis is cross-sectional dependence, which our analysis accepts. This implies that CD exists in our data set. This implies that SDI TNR, REC, TR and NREC have a problem of cross-sectional dependency. Table 3 Outcomes of CD test Test Stat. d.f. Prob. Breusch Pagan LM 328.48 66 0.00 Pesaran scaled LM 22.84 0.00 Pesaran CD 2.51 0.01 According to the existence of CD in the data, it is recommended that the stationarity of indicators and long-run association be checked utilizing the second-generation test for both unit root and cointegration. Table 4 shows that SDI is free from the problem of unit root at level according to both the CADF and CIPS tests, while other variables are integrated at first difference I (1). According to Table 5 , a statistically significant cointegration is observed at a 1% significance level between taken variables, including TNR REC, TR, NREC, and SDI. The results of CADF and CIPS suggested that the data have a unit root problem by which this study did not fulfill the assumption of least square, that data should not have problem of unit root. Hence, these results suggest that this study applies a test of cointegration to detect a long-run relationship among variables. Table 4 Results of Unit root tests CADF CIPS At level 1st Difference At level 1st Difference Integration SDI -2.689** -- -3.845* -- I (0) TNR -0.334 -4.022*** -2.341 -4.184*** I (1) REC -114 -3.473*** -2.124 -4.374*** I (1) NREC -1.985 -2.271** -1.183 -3.294*** I (1) TR 1.887 -3.943*** -0.948 -3.300*** I (1) Note: ***,** and * show significant levels of 1%, 5% and 10% respectively. According to Table 5 , a statistically significant cointegration is observed at a 1% significance level between many variables, including TNR, NREC, TR, REC, and SDI. Table 5 Westerlund Co-integration Test Results Statistics Value Z-value P-value Robust P-value G t -2.41 -2.19 0.01** 0.08* G a -6.26 -0.18 0.42 0.04** P t -5.04 -2.22 0.01** 0.01** Pa -7.32 -2.20 0.01** 0.00*** Note: ***,** and * show significant levels of 1%, 5% and 10% respectively. It is evident from Table 6 that Total natural resources rents have negative and significant impact on sustainable development. The results are similar with (Gyamfi et al., 2021 ; Zuo et al., 2021 ; Turan & Yanıkkaya, 2020 ; Kolstad & Wiig, 2009 ). Gyamfi et al. ( 2021 ) and Zuo et al. ( 2021 ) both found that natural resource rents contribute to environmental degradation, with Gyamfi et al. ( 2021 ) specifically highlighting the role of these rents in increasing pollution. Turan and Yanıkkaya ( 2020 ) further support this, showing that resource rents have a negative impact on public education and health expenditures, which are crucial for human capital formation. Kolstad and Wiig ( 2009 ) provides a potential explanation for these findings, suggesting that the resource curse is driven by patronage and rent-seeking behavior. These studies collectively suggest that TNR can hinder sustainable development by contributing to environmental degradation and undermining human capital formation. Sustainable development is positively affected by REC. The results consistently support the positive effect of renewable sources of energy uses on sustainable development (Güney, 2019 ; Candra et al., 2023 ). Sustainable development is positively affected by renewable resources-based energy consumption (Güney, 2019 ). Zhe et al. ( 2021 ) further underscores the positive influence of renewable resources-based energy on financial development, while Candra et al. ( 2023 ) emphasizes the role of renewable energy in economic growth and greenhouse gas emissions reduction. These findings collectively suggest that increasing REC is crucial for sustainable development. NREC has a positive and have significant impact on sustainable development. The results are in line with (Ohlan, 2016 ; Ivanovski et al., 2020; Adams et a., 2018; Mohammadi et al., 2023 ). Ohlan ( 2016 ) and Ivanovski et al. (2020) both found a positive long-run effect of NREC on GDP in non-OECD countries. Adams et al. ( 2018 ) further supported these findings, indicating that the utilization of non-renewable energy sources has a higher effect on fostering GDP. Mohammadi (2023) found that both REC and NREC have a favorable effect on economic growth. These studies collectively suggest that both types of energy consumption can contribute to GDP, with NREC having a more significant impact. TR has a positive and significant impact on sustainable development. The results are in line with (Vandenberg, 2017 ; Dao, 2015 ). Vandenberg ( 2017 ) suggested that trade can have a positive impact on employment, which is a key component of sustainable development. However, Dao (2014) argued that trade openness can lead to higher GDP, which is often seen as a key driver of sustainable development. These studies highlight the need for a nuanced understanding of the association between TR and sustainable development. The ECT shows the error correction term in the study which shows the dynamic stability of the model. A model's adjustment of coefficients indicates how much the model was adjusted in the previous period. The coefficient of the ECT is 0.2625, and it is statistically significant. This suggests that the model maintains its dynamic stability over time. According to the results of the study, the model of the study is stable. Table 6 PARDL Results Long Run Results Regressors Coefficient Std. Dev. Prob. TNR -0.027 0.001 0.008*** REC 3.064 0.568 0.000*** NREC 0.071 0.015 0.000*** TR 0.004 0.001 0.001*** Constant 0.024 0.041 0.553 Short Run Results D(TNR) 0.004 0.006 0.403 D(TNR(-1)) 0.008 0.008 0.315 D(REC) -0.671 0.384 0.086* D(REC(-1)) -0.442 0.236 0.066* D(NREC) -0.067 0.062 0.285 D(NREC(-1)) -0.013 0.039 0.749 D(TR) -0.001 0.001 0.167 D(TR(-1)) -0.001 0.001 0.582 ECT -0.265 0.136 0.055* Note: ***,** and * show significant levels of 1%, 5% and 10% respectively. 5. Conclusion and Recommendations Sustainable development is one of the feasible solutions to environmental issues. It is defined as a society's use of resources in such a way that future societies do not go without. As a result, this study is required to investigate other crucial aspects of sustainable development in MENA countries. It is worth noting that the majority of MENA countries have large natural resource reserves. As therefore, the primary goal of this study is to look into the impact of REC and NREC on sustainable development, as well as income and total rents from natural resources, in the MENA region. This study used REC, NREC, TNR and TR on sustainable development in case of MENA economies. The study used panel data from twelve MENA economies and the selected time period from 2000 to 2022. The study used a cross-sectional dependence test and confirmed the existence of cross-sectional dependency in the data. One can use second generation unit root tests and cointegration tests if data has problem of CD so, this study used CADF and CIPS to check the problem of unit root and Westerlund cointegration test is used for examining panel cointegration between taken variables. The results of unit root tests suggested to use Panel ARDL model to check the long and short run association among variables under the study. The results indicated that there is a negative impact of total rents from natural resources on sustainable development. Sustainable development is positively and significantly affected by REC in the long run while sustainable development is negatively and significantly affected by renewable energy in short run. NREC has a positive and significant impact on sustainable development in the long run. TR also has positive and significant impact on sustainable development in long run. According to the findings, the study proposed that if TNR has a negative impact on sustainable development, diversify revenue streams to lessen reliance on natural rents. Explore alternate economic activities and investments to provide resilience and financial sustainability. Furthermore, REC has a beneficial impact on sustainable development, indicating that policymakers should encourage investment in renewable energy sources to achieve economic and environmental sustainability. NREC has a positive and considerable impact on sustainable development, implying that using sustainable practices with NREC sources can improve economic sustainability. TR has a beneficial and considerable impact on sustainable development, suggesting that policymakers should increase trade openness to capitalize on it. Encourage international trade and trade agreements to enhance long-term development. Declarations Note ***,** and * show significant levels of 1%, 5% and 10% respectively. Author Contribution O.A.A: Writing – review & editing, Formal analysis, Data curation, Conceptualization, Methodology, Resources, Software, Writing – original draft. C.E: Conceptualization, Project administration, Supervision, Writing – review & editing. A.A.A: Data curation, Investigation, Writing – review & editing, Methodology. References Adams, S., Klobodu, E. K. M., & Apio, A. (2018). Renewable and non-renewable energy, regime type and economic growth. Renewable Energy , 125 , 755-767. 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Renewable Energy and CO 2 Emissions in Top Natural Resource Rents Depending Countries: The Role of Governance. Frontiers in Energy Research , 10 , 872941-872941. Timmerberg, S., Sanna, A., Kaltschmitt, M., & Finkbeiner, M. (2019). Renewable electricity targets in selected MENA countries–Assessment of available resources, generation costs and GHG emissions. Energy Reports , 5 , 1470-1487. Turan, T., & Yanıkkaya, H. (2020). Natural resource rents and capital accumulation nexus: do resource rents raise public human and physical capital expenditures?. Environmental Economics and Policy Studies , 22 , 449-466. Ullah, A., Ahmed, M., Raza, S. A., & Ali, S. (2021). A threshold approach to sustainable development: nonlinear relationship between renewable energy consumption, natural resource rent, and ecological footprint. Journal of Environmental Management , 295 , 113073. Ulucak, R., & Ozcan, B. (2020). Relationship between energy consumption and environmental sustainability in OECD countries: the role of natural resources rents. Resources Policy , 69 , 101803. Umar, M., Ji, X., Kirikkaleli, D., Shahbaz, M., & Zhou, X. (2020). Environmental cost of natural resources utilization and economic growth: can China shift some burden through globalization for sustainable development?. Sustainable Development , 28 (6), 1678-1688. Van Vuuren, D. P., Stehfest, E., Gernaat, D. E., Doelman, J. C., Van den Berg, M., Harmsen, M., ... & Tabeau, A. (2017). Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Global environmental change , 42 , 237-250. Vandenberg, P. (2017). Can trade help achieve the employment targets of the sustainable development goals? (No. 650). ADBI Working Paper. Vezzoli, C., Ceschin, F., & Diehl, J. C. (2015). Sustainable Product-Service System Design applied to Distributed Renewable Energy fostering the goal of sustainable energy for all. Journal of Cleaner Production , 97 , 134-136. WCED, S. W. S. (1987). World commission on environment and development. Our common future , 17 (1), 1-91. World Health Organization. (2016). Ambient air pollution: A global assessment of exposure and burden of disease. Xiaoman, W., Majeed, A., Vasbieva, D. G., Yameogo, C. E. W., & Hussain, N. (2021). Natural resources abundance, economic globalization, and carbon emissions: Advancing sustainable development agenda. Sustainable development , 29 (5), 1037-1048. Yahya, F., & Rafiq, M. (2019). Unraveling the contemporary drivers of renewable energy consumption: Evidence from regime types. Environmental Progress & Sustainable Energy , 38 (5), 13178. Yu-Ke, C., Awan, R. U., Aziz, B., Ahmad, I., & Waseem, S. (2022). The relationship between energy consumption, natural resources, and carbon dioxide emission volatility: empirics from G-20 economies. Environmental Science and Pollution Research , 1-9. Zhe, L., Yüksel, S., Dinçer, H., Mukhtarov, S., & Azizov, M. (2021). The positive influences of renewable energy consumption on financial development and economic growth. Sage Open , 11 (3), 21582440211040133. Zuo, S., Zhu, M., Xu, Z., Oláh, J., & Lakner, Z. (2021). The dynamic impact of natural resource rents, financial development, and technological innovations on environmental quality: Empirical evidence from BRI economies. International Journal of Environmental Research and Public Health , 19 (1), 130. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4986566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":356624520,"identity":"804f9bd9-6eed-4ad3-9fc0-c1b4ad73659d","order_by":0,"name":"Omar Ahmed Abdulraqeb","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYLCCDwY2cmzMjI2PIdwEwjoYZ1SkGfOzMx82JloLM8+Zw4kz+9nSpInSwi92+NkH3jZmxg2HecyqC3ccZuBnzzFg+LkDtxbJ2WnGMyTb2JgNgFpuzzxzmEGy540BY+8Z3FoMbicYMxi28bCBtfC2HWYwuJFjwMzYhk9L+meGxDYJHpCWYpAWe8JacowZDpwxkJBsZktjBtsiQUCL5OycYsaGigQDfmbmw9K8bek8EmeeFRzsxaOFXzp9M/Mfg//1bfwHGz/ztlnL8bcnb3zwE48WDMADIg6QoGEUjIJRMApGARYAAHYZS7beJBpQAAAAAElFTkSuQmCC","orcid":"","institution":"Hunan University","correspondingAuthor":true,"prefix":"","firstName":"Omar","middleName":"Ahmed","lastName":"Abdulraqeb","suffix":""},{"id":356624521,"identity":"793ac9bb-4984-4ac3-ba1e-06acfffbb8c5","order_by":1,"name":"Cao Erbao","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Cao","middleName":"","lastName":"Erbao","suffix":""},{"id":356624522,"identity":"0f618194-4f76-4ca5-80bc-47c618fe2a27","order_by":2,"name":"Abdullah Aloqab","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Aloqab","suffix":""}],"badges":[],"createdAt":"2024-08-27 19:33:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4986566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4986566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74515825,"identity":"07caed9e-d452-483c-9574-ce52e10a3fea","added_by":"auto","created_at":"2025-01-23 04:46:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":592698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4986566/v1/4652598c-cd92-4cd3-a217-c1dc3081d46a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Natural resource rents and sustainable development in MENA Economies: An empirical study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSustainable development is one of the feasible solutions to environmental issues. It is defined as employing society's resources in such a way that future societies do not go without. The World Commission on Environment and Development's \"Our Common Future\" study roughly defines sustainable development as: in order for future societies to meet their requirements, we must reduce the environmental repercussions of current consumption. In this context, sustainable development consists of three components: social, environmental, and economic. Meanwhile, energy use affects the economy, society, and the environment. However, nonrenewable energy use is damaging to our planet. Such are conventional sources of energy that utilize fossil fuels, that have a favorable impact on GDP but have produced environmental contamination through waste generated by fossil fuels. Almost all countries use fossil fuels to fulfill their need for energy (Elum \u0026amp; Momodu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the World Health Organization (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), air pollution is one of the most significant risk factors for human health all around the world, alongside hypertension and diabetes. To minimize environmental degradation, it is better to use REC than NREC. Using REC can help meet the growing energy needs while also achieving sustainable development (Ozturk \u0026amp; Acaravci, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, according to projections from scientific studies, the total supply of power in 2100 may consist of 30\u0026ndash;65% REC, which would lead to a substantial reduction in carbon emissions (Van et al., 2017).\u003c/p\u003e \u003cp\u003eThe energy sector represents the largest portion of the world economy (Vezzoli et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Also, energy is a key contributor to economic growth (GDP), which is connected to all aspects of development, lowering poverty, and raising the standard of living (Bilgili et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kaygusuz \u0026amp; Bilgen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ockwell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yahya \u0026amp; Rafiq, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In other words, modern society satisfies its own demands; it is depleting future generations' access to a source of production that is getting more and more polluted. Hence, energy serves as a link between the current and next generations. Therefore, the preferences for energy of the current generation are influencing the environment as well as consumption of goods and services by future generations. As a result, sustainable development and energy consumption are closely related. Most countries from the Middle East and North Africa (MENA) have larger oil and natural gas reserves, and they heavily rely on oil rents. In MENA countries during the period 2000\u0026ndash;2019 were 22.22 percent, 16.1 percent, 23.19 percent, 48.24 percent, 46.42 percent, 48.74 percent, 35.87 percent, 25.86 percent, 38.39%, and 21.14 percent of Gross Domestic Product (GDP). Furthermore, MENA economies' average TNR during 2000\u0026ndash;2019 are lower than 6% (World Bank, 2022). Timmerberg et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the majority of MENA economies have a miserable proportion of REC in their overall energy mix.\u003c/p\u003e \u003cp\u003eKeeping in view the energy and environmental degradation situation in MENA, for example, Arouri et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) conducted research in MENA economies to analyze the effect of energy use and per capita income on carbon emissions. Alkasasbeh et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the effects of income and energy use on carbon emissions in the Middle East. Gorus and Aslan (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted research to determine the economic factors that contribute to the degradation of the environment in the MENA countries. Mahmood et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied the effect of oil and natural gas on carbon emissions in MENA economies. None of the studies in the literature determined the impact of TNR on sustainable development, as most of the research studies used CO2 as a proxy for environmental degradation or environmental sustainability, for instance, Bilgili et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas this novel study investigates the impact of TNR on sustainable development in the MENA region along with other important factors of sustainable development. This study proxy sustainable development with sustainable development index (SDI) which is the ecological efficiency of the human development index. SDI is gauged through the ratio of human development index to ecological overshoot in a country.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eSeveral studies have confirmed that TNR reduces environmental degradation, including those of emerging and developed nations (Altinoz \u0026amp; Dogan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bekun et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Umar et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ulucak and Ozcan (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found a one-way causal association going from total rents from natural resources to environmental degradation in the OECD countries. Ullah et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the world\u0026rsquo;s top REC using countries and concluded that natural rent causes an increase in emissions of carbon leads to degradation of environment. Yu-Ke (2021) found that minerals, oil, and forest rent contribute significantly to environmental degradation in G-20 countries. Shen et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) got a positive linkage among rents from natural resources and environmental sustainability in China. Zuo et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) taken EFP and check the impact of rents from natural resources on EFP for 90 countries involved in the Belt and Road Initiative. The study covered time period from 1991\u0026ndash;2018 and concluded that total rents from natural resources to the environmental quality.\u003c/p\u003e \u003cp\u003eThe empirical results found the positive impact of REC on environmental degradation in case of Chile (Kirikkaleli et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ben et al. (2016) found one directional causality from renewable energy use to environmental degradation in North African countries. Bilgili et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) utilized panel data analysis to demonstrate a negative correlation among renewable energy and environmental degradation across organizations for economic cooperation and development countries. In case of Turkey the REC declines emissions of greenhouse gases (B\u0026ouml;l\u0026uuml;k \u0026amp; Mert, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The countries with high law and accountability have negative association between renewable energy use and environmental degradation (Szetela, 2022) while the studied by Ullah et al., (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) concluded that REC use has hinger the ecological footprint in case of top REC consumer countries of the World. The study found for fastest developing economies where Paramati et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigated the impact of REC use on GDP along with the environmental quality and they found the favorable impact of REC use on GDP as well as on environmental quality.\u003c/p\u003e \u003cp\u003ePrior researchers studied the association among REC use on environmental quality as well as on economic growth. G\u0026uuml;ney (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) toke developed and developing countries and studies that sustainable development is affected by REC and REC use. The study selected 73 developing countries from the world and 40 developed economies and found positive association among renewable, NREC use and sustainable development in case of developed and developing countries. In case of European Union economies, Bekun et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) studied the causal association between economic growth, nonrenewable energy, and environmental quality. The study found the positive impact of NREC use and income of the country on environmental degradation. Opeyemi (2019) investigated the association among NREC and REC use and carbon emissions for Nigeria taking the period 1987\u0026ndash;2016. The conclusions indicated that the environment is degrading by non-renewable energy use.\u003c/p\u003e \u003cp\u003ePrevious studies examined the consequence of trade openness on sustainable development in which Cole and Elliott (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) examined the trade and environment composition effect. Omri (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated the impact of CO2 on TR and other variables for MENA economies. This study used simultaneous equation model. The results indicated that there is an inverse association between trade and environmental degradation. Shahbaz et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) explored the association between TR and environmental degradation by incorporating GDP as potential variable in study for three groups of 105 countries. The study grouped the sample into countries with higher, middle, and lower incomes for the period 1980 to 2014. The outcomes concluded the inverted U-shaped association between trade and quality of environment for all samples. Shahbaz et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) took data for 105 developed and developing countries to investigate how trade affects the quality of environmental. They used Padroni and Wester Lund test for checking the long run impact. The results indicated TR hindered the quality of environment. Hasanov et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigated the effect of trade openness on environmental degradation in case of oil exporting countries and found the insignificant impact of trade on environmental degradation. Ling et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) concluded that TR improve environmental quality in case of Malysia. Mahmood et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) explored the effect of trade on environmental quality for Tunisia. The study used ARDL approach and selected time period 1971\u0026ndash;2014. The results indicated that there is positive and insignificant effect of TR on CO2.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eUsing a panel data set for Middle East and North African economies covering time from 2000 and 2022. This study analyzes panel data for Algeria, Egypt, Iran, Iraq, Kuwait, Morrocco, Oman, Qatar, Saudi Arabia, Turkey, and United Arab Emirates. The taken variables for the study are sustainable development index, TNR, REC, NREC use and TR. Sustainable development index is used for sustainable development which is measure through the ratio of human development index over ecological overshoot. Renewable energy use is taken as the combination of hydro energy, wind energy and nuclear energy consumption. NREC is taken as the combination of oil energy, gas energy and coal energy consumption. This study used sustainable development as dependent variable and the others are used as independent variables. The TNR, REC and NREC are treated as main variables. Most of the researchers use TR as control variable (Xiaoman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khalid et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), so this study uses TR as control variable. These variables are taken from the World Development Indicators (WDI) databank (World Bank, 2023) and British Petroleum (BP) online database (BP, 2023). A detailed overview of these variables can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePrior to performing unit root testing, cross-sectional dependence (CD) is evaluated. CD results from variables such as economic integration and residual interdependence. The presence of CD is determined with the Pesaran CD test. Addressing CD is critical since it affects the consistency of unit-root and the cointegration results. Following the CD evaluation, the study uses unit root tests using panel data. A first-generation technique does not address CD issues in datasets due to its shortcomings (Im et al., 2003). For this reason, it is crucial to ensure that data are stationary. As a\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable Development Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasured in sustainable development Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Natural Resources Rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal natural resources rents (measured in % of GDP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenewable Energy Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombination of Hydro Energy, Wind Energy and Nuclear energy and measured as Exajoule\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade Openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrade (% to GDP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Renewable Energy Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombination of Oil Energy, Gas Energy and coal energy and measured as Exajoule\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eresult of CD difficulties, the second-generation unit root test for panel data are CADF and CIPS presented by (Pesaran, 2007). This method is expected to provide dependable and consistent stationarity features. Panel cointegration of underlying variables is used to forecast long-term values. Traditional cointegration approaches may produce incorrect findings in the presence of CD and heteroscedasticity. The present study uses Westerlund and Edgerton's (2008) panel cointegration test to analyze CD, autocorrelation, and structural breakdowns. The Panel ARDL model presented by Pesaran and Smith (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Pesaran et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) used to estimate long-run estimates when variables are cointegrated over the long run. Moreover, the cointegration process does not lead to simultaneity biases in these estimates. Also, the long-run estimates are not affected by autocorrelation. Thus, this study developed the following mode:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SDI=f(TNR,\\:REC,\\:NREC,TR)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe empirical model can be expressed as follows after considering panel settings:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{SDI}_{it}=\\:{\\beta\\:}_{0i}+{\\beta\\:}_{1i}{TNR}_{it}+{\\beta\\:}_{2i}{REC}_{it}+{\\beta\\:}_{3i}{NREC}_{it}+{\\beta\\:}_{4i}{TR}_{it}+\\:{\\in\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i represents the cross-section units (i\u0026thinsp;=\u0026thinsp;1, 2, 3,. .. N), t represents the time period (t\u0026thinsp;=\u0026thinsp;1, 2, 3, ... T) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\:\\)\u003c/span\u003e\u003c/span\u003eshows intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1},{\\beta\\:}_{2},{\\beta\\:}_{3},{\\beta\\:}_{4}\\)\u003c/span\u003e\u003c/span\u003e shows the slopes and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\in\\:\\:\\)\u003c/span\u003e\u003c/span\u003erepresents error term. In addition, SDI stands for Sustainable Development Initiative, TNR for Total Natural Resource Rent, REC for Renewable Energy Use, NREC for Non-Renewable Energy Use, and TR for Trade Openness. The study covers twelve MENA countries.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e exhibits the descriptive statistics of the variables. The sustainable development index (SDI) is measured as an index number which shows the means value of SDI is 0.51. the maximum SDI is 0.79 and minimum values is 0.10. TNR is gaged as percentage in GDP. The mean value of TNR is 23.23%. The maximum % of TNR is GDP is 65.32% and minimum values is 1.79%. The renewable energy consumption and nonrenewable energy consumption are taken in exajoule. The mean values of REC and NREC are 0.08 and 3.06 exajoule respectively while the maximum value of REC is 0.14 and NREC is 12.01 and minimum value of REC is 0.04 and NREC is 0.40 exajoule. TR is gaged as percentage of GDP. The mean value of TR is 76.40% and the maximum Trade value is 172.80 and minimum value of Trade is 29.87%. The standard deviation shows the fluctuation in data which shows the Trade has more variation than other variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNREC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e172.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CD test results are revealed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which confirm the presence of the CD in our data set. The CD test's alternative hypothesis is cross-sectional dependence, which our analysis accepts. This implies that CD exists in our data set. This implies that SDI TNR, REC, TR and NREC have a problem of cross-sectional dependency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes of CD test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch Pagan LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran scaled LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the existence of CD in the data, it is recommended that the stationarity of indicators and long-run association be checked utilizing the second-generation test for both unit root and cointegration. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that SDI is free from the problem of unit root at level according to both the CADF and CIPS tests, while other variables are integrated at first difference I (1). According to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a statistically significant cointegration is observed at a 1% significance level between taken variables, including TNR REC, TR, NREC, and SDI. The results of CADF and CIPS suggested that the data have a unit root problem by which this study did not fulfill the assumption of least square, that data should not have problem of unit root. Hence, these results suggest that this study applies a test of cointegration to detect a long-run relationship among variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Unit root tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCIPS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAt level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1st Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntegration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.689**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.845*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.184***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.473***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.374***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.271**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.943***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.300***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***,** and * show significant levels of 1%, 5% and 10% respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, a statistically significant cointegration is observed at a 1% significance level between many variables, including TNR, NREC, TR, REC, and SDI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWesterlund Co-integration Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***,** and * show significant levels of 1%, 5% and 10% respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt is evident from Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e that Total natural resources rents have negative and significant impact on sustainable development. The results are similar with (Gyamfi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zuo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Turan \u0026amp; Yanıkkaya, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kolstad \u0026amp; Wiig, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Gyamfi et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Zuo et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) both found that natural resource rents contribute to environmental degradation, with Gyamfi et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) specifically highlighting the role of these rents in increasing pollution. Turan and Yanıkkaya (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) further support this, showing that resource rents have a negative impact on public education and health expenditures, which are crucial for human capital formation. Kolstad and Wiig (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) provides a potential explanation for these findings, suggesting that the resource curse is driven by patronage and rent-seeking behavior. These studies collectively suggest that TNR can hinder sustainable development by contributing to environmental degradation and undermining human capital formation.\u003c/p\u003e \u003cp\u003eSustainable development is positively affected by REC. The results consistently support the positive effect of renewable sources of energy uses on sustainable development (G\u0026uuml;ney, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Candra et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sustainable development is positively affected by renewable resources-based energy consumption (G\u0026uuml;ney, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Zhe et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further underscores the positive influence of renewable resources-based energy on financial development, while Candra et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasizes the role of renewable energy in economic growth and greenhouse gas emissions reduction. These findings collectively suggest that increasing REC is crucial for sustainable development.\u003c/p\u003e \u003cp\u003eNREC has a positive and have significant impact on sustainable development. The results are in line with (Ohlan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ivanovski et al., 2020; Adams et a., 2018; Mohammadi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ohlan (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Ivanovski et al. (2020) both found a positive long-run effect of NREC on GDP in non-OECD countries. Adams et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) further supported these findings, indicating that the utilization of non-renewable energy sources has a higher effect on fostering GDP. Mohammadi (2023) found that both REC and NREC have a favorable effect on economic growth. These studies collectively suggest that both types of energy consumption can contribute to GDP, with NREC having a more significant impact.\u003c/p\u003e \u003cp\u003eTR has a positive and significant impact on sustainable development. The results are in line with (Vandenberg, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dao, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Vandenberg (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) suggested that trade can have a positive impact on employment, which is a key component of sustainable development. However, Dao (2014) argued that trade openness can lead to higher GDP, which is often seen as a key driver of sustainable development. These studies highlight the need for a nuanced understanding of the association between TR and sustainable development.\u003c/p\u003e \u003cp\u003eThe ECT shows the error correction term in the study which shows the dynamic stability of the model. A model's adjustment of coefficients indicates how much the model was adjusted in the previous period. The coefficient of the ECT is 0.2625, and it is statistically significant. This suggests that the model maintains its dynamic stability over time. According to the results of the study, the model of the study is stable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePARDL Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLong Run Results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegressors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShort Run Results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TNR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TNR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(REC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(REC(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NREC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(NREC(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***,** and * show significant levels of 1%, 5% and 10% respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cp\u003eSustainable development is one of the feasible solutions to environmental issues. It is defined as a society's use of resources in such a way that future societies do not go without. As a result, this study is required to investigate other crucial aspects of sustainable development in MENA countries. It is worth noting that the majority of MENA countries have large natural resource reserves. As therefore, the primary goal of this study is to look into the impact of REC and NREC on sustainable development, as well as income and total rents from natural resources, in the MENA region. This study used REC, NREC, TNR and TR on sustainable development in case of MENA economies. The study used panel data from twelve MENA economies and the selected time period from 2000 to 2022. The study used a cross-sectional dependence test and confirmed the existence of cross-sectional dependency in the data. One can use second generation unit root tests and cointegration tests if data has problem of CD so, this study used CADF and CIPS to check the problem of unit root and Westerlund cointegration test is used for examining panel cointegration between taken variables. The results of unit root tests suggested to use Panel ARDL model to check the long and short run association among variables under the study. The results indicated that there is a negative impact of total rents from natural resources on sustainable development. Sustainable development is positively and significantly affected by REC in the long run while sustainable development is negatively and significantly affected by renewable energy in short run. NREC has a positive and significant impact on sustainable development in the long run. TR also has positive and significant impact on sustainable development in long run. According to the findings, the study proposed that if TNR has a negative impact on sustainable development, diversify revenue streams to lessen reliance on natural rents. Explore alternate economic activities and investments to provide resilience and financial sustainability. Furthermore, REC has a beneficial impact on sustainable development, indicating that policymakers should encourage investment in renewable energy sources to achieve economic and environmental sustainability. NREC has a positive and considerable impact on sustainable development, implying that using sustainable practices with NREC sources can improve economic sustainability. TR has a beneficial and considerable impact on sustainable development, suggesting that policymakers should increase trade openness to capitalize on it. Encourage international trade and trade agreements to enhance long-term development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eNote\u003c/h2\u003e \u003cp\u003e***,** and * show significant levels of 1%, 5% and 10% respectively.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eO.A.A: Writing \u0026ndash; review \u0026amp; editing, Formal analysis, Data curation, Conceptualization, Methodology, Resources, Software, Writing \u0026ndash; original draft. C.E: Conceptualization, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing. A.A.A: Data curation, Investigation, Writing \u0026ndash; review \u0026amp; editing, Methodology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, S., Klobodu, E. K. M., \u0026amp; Apio, A. (2018). 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The dynamic impact of natural resource rents, financial development, and technological innovations on environmental quality: Empirical evidence from BRI economies. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 130.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sustainable development, renewable and non-renewable energy, natural resource rents, trade openness","lastPublishedDoi":"10.21203/rs.3.rs-4986566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4986566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and its effects around the globe are demanding sustainable development, which encompasses socio-economic development along with taking into account the environmental aspect. The Middle East and North African economies (MENA) possess substantial reserves of natural resources. These natural resources played an important role in the development of this region, and studies are scarce in this regard for this region. Therefore, this study investigates the impact of natural resource rents on sustainable development in MENA. This study used the Sustainable Development Index, which incorporates socio-economic and environmental aspects. Thus, this study is different from other studies in this regard in the MENA region. Additionally, this study also considered other vital factors of sustainable development highlighted in the literature. These factors are renewable energy (REC), non-renewable energy (NREC), and trade openness (TRADE). This is a panel study; thus, a cross-sectional dependence test is carried out, which indicates that second-generation unit root tests have to be used for unit root problems. Likewise, the Westerlund cointegration test is used for examining panel cointegration between variables. Panel autoregressive distributed lagged (PARDL) technique is carried out for long- and short-run effects of explanatory variables on sustainable development. The results indicate that natural resource rents (TNR) have a negative impact on sustainable development, while REC, NREC, and TRADE positively influence sustainable development in the long run.\u003c/p\u003e","manuscriptTitle":"Natural resource rents and sustainable development in MENA Economies: An empirical study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-01 07:40:40","doi":"10.21203/rs.3.rs-4986566/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7674b80e-3dbc-4095-bdb0-ab9fe5ae3713","owner":[],"postedDate":"October 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T04:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-01 07:40:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4986566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4986566","identity":"rs-4986566","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}