Exploring the dynamic nexus between urbanization,energy efficiency,renewable energies,economic growth,with ecological footprint: A panel cross-sectional autoregressive distributed lag evidence along Middle East and North Africa countries

Abstract

The release of greenhouse gases (GHG) is acknowledged as a serious hazard that fuels climate change (CC). The Middle East and North Africa (MENA) countries face particular environmental challenges due to its contribution to carbon dioxide emissions (CO₂). There is, however, a scarcity of literature about the region's environmental challenges, despite its vulnerability to the consequences of CC. Therefore, the purpose of this research is to look at various solutions to the CC issue in the MENA area, that is, energy efficiency (EE), economic development, urbanization, fossil fuel (FF) usage, and renewable energies (RE). Further, the study seeks to evaluate CO₂ emissions in the region by establishing a RE index. By applying the fresh technique known as the cross-sectional autoregressive distributed lag estimator to panel data for 18 MENA countries from 1990 to 2019, this study quantifies the impact of these variables on CO₂ emissions. The findings reveal that the explanatory variables’ significance varies across regional panel clusters, with economic growth having a significantly positive effect on CO₂ emissions in the long run. Moreover, the study rejects the Environmental Kuznets Curve hypothesis validation and highlights the importance of EE in boosting environmental quality in the long and short runs. Additionally, the outcomes suggest that RE contributes to reducing CO₂ emissions, while the urbanization level and FF consumption adopted by MENA countries do not improve their environment. Thus, mitigating the negative consequences of CC in the MENA area necessitates encouraging EE and lobbying for a shift from FF to RE sources.

Keywords

CS-ARDL estimates energy efficiency PCA renewable energy urbanization

Introduction

Climate change (CC) has become one of the greatest critical environmental matters, with increasing greenhouse gas (GHG) emissions recognized as its primary cause.^1,2 CO₂ emissions, the major component of GHG emissions, are known to have a significant impact on CC.³ The detrimental effects of CC are evident in the environment, society, and economy globally.⁴ Developing countries are expected to face more severe consequences of CC as they struggle to cope with the challenges that arise.⁵ The past century has seen economic growth and transformation in numerous national and regional economies, albeit at the cost of the environment. According to statistics from the International Energy Agency⁶ since 2010, CO₂ emissions from fossil fuel (FF) energy sources have shown a worrying upward trend, increasing by 0.9% in 2021 and reaching 33 gigatons of CO₂ equivalent.

Concerns regarding the impact of increased CO₂ emissions on CC have led many countries and international organizations to commit to reducing emissions. Environmental advocates and experts in ecology have called upon the global community to implement measures aimed at restricting CO₂ emissions into the atmosphere.⁷ In response, nations have participated in several international conventions, that is, the Montreal and Kyoto Protocols, the Stockholm Conference, and the Paris Agreement, to address the effects of CC and limit GHG emissions.⁸

For the last decade, global attention has been directed toward achieving economic and environmental sustainability. However, the unforeseen effects of technological advancements in the energy and industrial sectors, including degradation of environmental quality, mass communication, urbanization, and transportation infrastructure, have been a growing concern.⁹ In this regard, energy is considered a crucial resource for a region's strategic development, and the utilization of FF remains the primary driver of economic growth in the current technological era.¹⁰

The adoption of intensive energy production systems since the beginning of the industrial revolution has resulted in environmental degradation. CC has been identified as a primary consequence of the increasing global average annual temperatures, which are projected to grow by 3–5°C in the coming century. Studies have indicated that the Middle East and North Africa (MENA) region is particularly vulnerable to the impacts of CC.¹¹ The MENA economic monitor by the World Bank reports that the implementation of reforms has led to a rise in economic growth to 3% in 2018 and 3.2% in 2019. This growth has been observed in certain MENA countries that have experienced the adverse effects of revolutions, war, violence, and limited FF prices.

The MENA region is home to the greatest stores of oil and natural gas in the globe.¹² In fact, according to Dudley,¹³ the area in question holds a majority of the oil reserves of the world, specifically 60%. The area exhibits diversity in its natural resource availability, with some Gulf countries being prosperous in oil and gas reserves while others are resource scarce. MENA nations are thought to be among the wealthy in oil and gas resources but among the lowest in terms of water resources worldwide. According to the findings of Chentouf and Allouch,¹⁴ the MENA region possessed 51.2% of the world's confirmed oil deposits and 47% of the globe's confirmed gas stores. These nations need to increase their consumption of FF to achieve economic growth and expand urbanization, which will lead to an improvement in their overall standard of living.¹⁵ Research has shown that energy usage and economic development are the main factors behind the release of CO₂ emissions.^16–20 The MENA region is presently undergoing economic modernization and industrialization efforts, which has resulted in a rise in CO₂ emissions (Figure 1). According to the World Development Indicators¹² (WDI), MENA countries contributed 7.44% of global CO₂ emissions in 2018, which is significant relative to their size. Notably, Qatar, Kuwait, and the UAE ranked among the top five major emitters per capita in 2018, emitting 32.4 CO₂/capita (1st), 21.6 t MENA region have high current CO₂ emission levels, they have not been considered historically/capita (4th), and 20.8 CO₂/capita (5th), respectively.^12,14 Furthermore, CO₂ emissions per capita for MENA countries have been higher than the world mean since 1993, as per World Bank data. The nations in the MENA area involved in oil and gas production exhibit elevated levels of CO₂ emissions. Despite their emissions accumulating since the beginning of the industrial revolution, they have not been held responsible for the occurrence of global warming in history. However, the endeavor to decrease CO₂ and GHG emissions by a particular nation can potentially aid in mitigating CC, provided that it is complemented by the efforts of other nations.

Figure 1.

The emission trend of CO₂ per capita in the sample countries and globe.¹²

MENA countries undergoing economic growth are generating employment opportunities, resulting in a human transition toward urbanization. The migration of the workforce to urban centers has caused an ascend in energy requirements, leading to elevated levels of ambient air pollution.²¹ In 2020, the urban population in MENA accounted for 66% of the total regional population, which is higher than the worldwide average of 55%, as reported by the World Bank.¹² The rate of urbanization in the area has increased by an average of 2.5% since 2000, which is greater than the worldwide average of 2.1% (Figure 2). Urbanization has led to improved economic and industrial aggregation, where people centralize their resources and talents, acting as a leading stimulus for the economy and innovation.²² Today, cities produce over 80% of the world's GDP, contributing to sustainable growth if managed efficiently to increase productivity and innovation.¹² Despite urbanization has advantages, it utilizes resources like energy and raw materials while generating waste such as gas, water, and land.²³ Accordance to official statistics, approximately 75% of the FF energy demand and 70% of the renewable energies (RE) demand are consumed by urban areas.¹² Urban regions are accountable for more than 70% of worldwide GHG emissions.²⁴ MENA countries face the challenge of meeting the rising energy needs of their urban regions while also striving to enhance environmental conditions. In response, there has been an increase in interest in the potential of RE solutions.

Figure 2.

The trend of urbanization in the world and MENA countries with growth rate.¹²

Because it can lower GHG emissions, is environmentally friendly, and promotes the use of clean, safe, and more affordable energy while promoting economic development and innovation, the RE sector is appealing.^2,25,26 The importance of growing this industry in light of its advantages for the economy and environment has been acknowledged by policymakers. Recent research has shown how successful RE is in lowering CO₂ emissions and mitigating the consequences of CC.^27–34

In this context, the MENA region is recognized as a significant market for RE as its abundant natural resources like biomass, wind, solar, hydropower, and geothermal energy. RE, in particular, is playing the main act in the economic growth of MENA nations, which continue to depend largely on conventional energy sources to fulfill rising demand. Rapid population and economic growth in certain MENA nations have prompted them to prioritize the expansion of RE as a possible energy source. The documented potential of RE to mitigate GHG emissions and foster sustainable development is widely acknowledged.^35–38 Notable is the fact that Net Oil Importing nations pay exorbitant prices for FFs, whereas Net Oil Exporting nations must consider the opportunity cost of producing FFs. Therefore, investing in RE development can create new opportunities for MENA countries to diversify their energy mix and strengthen their economies in the long term.

Biomass, geothermal, sun, wind, and hydropower are whole kinds of RE that may be harnessed and utilized. The proportion of net energy exporters and importers in the MENA area has a distinct influence on the spread of RE. Energy-exporting nations rely heavily on FFs, whereas energy-importing nations have demonstrated a greater commitment to the shift to RE. Developing RE sources is currently the greatest challenge for MENA nations. Charfeddine and Kahia³⁹ identified biomass, hydropower, wind, and solar as the region's most prevalent energy sources. The MENA region's installed RE capacity is estimated to be around 11 GW, with wind energy constituting the most significant proportion (7.5 GW), trailed by photovoltaic solar (1.7 GW), biomass production and garbage (650 MW), geothermal power (775 MW), as well as concentrated solar energy (347 MW).

The MENA area exhibits a comparatively low amount of investment in renewable technologies, resulting in a mere 4% contribution of renewables to the region's overall power generation. Notably, hydropower constitutes the majority of this contribution, accounting for 81%.⁶ Insufficient progress in RE legislation, initiatives, and credible targets can be attributed to the spread of inexpensive FF in the region. The MENA region exhibits significant potential for RE sources, owing to its advantageous climatic and geographical features that facilitate the production of solar, wind, and geothermal energy. The majority of the area is situated in the sunbelt, affording optimal circumstances for solar power generation due to its global horizontal irradiance (GHI) reaching a maximum of 2600 kWh/m²/y and direct normal irradiance (DNI) reaching up to 2800 kWh/m²/y.^40,41 Innovation could rise the consumption of RE through lowering expenses and enhancing efficiency.⁴² Despite some governments underrating the full potential of renewables in the area, the MENA nations have declared ambitious objectives for the employment of RE sources, in reaction to international political demands, the advantages of renewable energy (RE), and increased recognition of the challenges posed by CC.^43,44

Achieving decarbonization on a worldwide extent necessitates the involvement of energy conservation as a fundamental component. To address this issue, cost-effective strategies such as improving energy efficiency (EE) are suggested.⁴⁵ EE indicates the capability to achieve the same amount of output while using a reduced level of energy.⁴⁶ Robust policies that endorse EE are crucial in attaining the 1.5°C target and curtailing energy and climate mitigation expenses. This is because enhanced EE can account for up to 50 percent of the emission reductions mandated by the Paris Agreement. Given the efficacy of EE in CO₂ emissions reduction, a crucial question arises in the MENA region: what effect does EE have on CO₂ emissions in these nations?

Although MENA countries’ EE improvements are below the global average of 1.7%, the UN's SDG7 progress report indicates that between 2010 and 2018, MENA countries’ EE improved by an average of 1.3%.⁶ However, the high-energy sector subsidies in MENA countries diminish the significance of EE as a key element in diminishing CO₂ emissions. Consequently, the impact of EE improvements on lowering CO₂ emissions might be negligible at best.

Given the preceding assertions, the present investigation tries to determine the extent of CO₂ emissions in the MENA area through the development of a combined indicator of RE that takes into account their environmental challenges. The composite index will include biogas, solar, wind, and hydropower, among others. MENA region by creating a composite index of RE sources that take into account their environmental challenges. The composite index will include biogas, solar, wind, and hydropower, among other energy sources. The focus of this study will be on the environmental implications of CO₂ emissions, with consideration given to the escalating environmental impact of existing energy policies in MENA nations, as well as the potential for the region to emerge as a prominent player in environmental monitoring on a global scale. Furthermore, this study explores the effects of urbanization, FF utilization, and EE on CO₂ emissions, drawing on the nexus between economic advancement and CO₂ emissions through the utilization of the novel second-generation approach of CS-RADL. The present study tests the direct and indirect impacts of said variables on CO₂ emissions. According to the Environmental Kuznets Curve (EKC) theory, rising economic growth rates are correlated with increased atmospheric carbon dioxide levels.

Literature review

In current decades, there has been a notable surge of scholarly attention directed toward comprehending the interconnections among economic expansion, energy utilization, demographic trends, urban development, and ecological conditions. According to Jalilvand⁴⁷ and Shahbaz⁴⁸ GHG emissions from energy consumption are projected to increase by 20% by 2035 compared to 2010 levels. For the MENA region, this increase is predicted to be even greater at 47%. Hence, it is essential to survey the interconnections among energy utilization, economic advancement, and CO₂ emissions, considering the fact that CO₂ stands as the most prominent GHG.

CO₂ emissions and RE

Several experimental assessments have been conducted to check the influence of RE consumption on GHG emissions, but conclusive evidence is still lacking. These studies have revealed that the consumption of sustainable energy sources can cause a decline in overall emissions, while others have found the opposite. Adebayo, Rjoub⁴⁹ conducted a study on the asymmetric impacts of RE consumption on carbon dioxide release in Sweden. The researchers used the quantile regression technique and observed that the utilization of RE at both lower and higher quantiles has a mitigating influence on air pollution. Also, in a separate study, Haldar and Sethi⁵⁰ conducted an independent study to investigate the moderating impact of institutional quality on the nexus between energy and CO₂, along with other variables such as RE, in 39 developing nations spanning from 1995 to 2017. The outcomes of their research indicated that RE consumption has a long-run significant effect in lowering emissions. In a different context, Bekun, Alola⁵¹ discovered that RE consumption decreased CO₂ emissions in sixteen European Union countries. According to Bilgili, Koçak,⁵² the use of RE has helped reduce carbon emissions across 17 OECD nations. Additionally, their results disprove the EKC theory. Zoundi⁵³ demonstrated that RE reduced CO₂ emissions in African nations. Kahia, Kadria³⁵ demonstrated that RE promoted economic prosperity and reduced CO₂ emissions in MENA nations, whereas Hu, Xie⁵⁴ investigated the nexus between RE and CO₂ emissions for 25 developing countries and revealed that the adoption of RE reduced CO₂ emissions.

Furthermore, multiple studies underline the minor or even positive impacts of RE use on the spread of GHG emissions, with results varying by nation. Khattak, Ahmad⁵⁵ investigated the interaction between the consumption of RE and various variables with air pollution for BRICS countries. Outcomes showed that RE had not mitigated the CO₂ emission in South Africa. Nguyen and Kakinaka⁵⁶ searched the linkage between RE use and CO₂ emission in 107 countries with various income groups. Their findings revealed that RE consumption is positively related with CO₂ emission in less-income group nations. Saidi and Omri⁵⁷ have reported that RE outlays have caused a decline in CO₂ emissions in several countries. Although, findings from South Korea and Netherlands pointed to rising emissions of CO₂ emissions. Due to the results of Nguyen and Kakinaka,⁵⁶ the consumption of RE positively is related to GHG emissions in nations with lower income levels. In 5 North African countries, the research conducted by Ben Jebli and Ben Youssef⁵⁸ showed that the consumption of RE promotes a rise in CO₂ emissions. The study conducted by Apergis and Payne⁵⁹ revealed that the utilization of RE did not result in immediate reductions in emissions across the 19 advanced and developing nations that were examined.

Urbanization-CO₂ emissions linkage

Urbanization has been identified as a significant factor in the MENA region that explains the environmental quality. Péridy and Brunetto⁶⁰ claim that 6–25 million people in urban MENA nations are at risk of coastal flooding if temperatures rise by just 1–3°C. The United Nations’ 2018 version of the World Urbanization Prospects indicates that the number of urban dwellers in the MENA region has been increasing steadily over the years. In 2000, there were 188 million urban dwellers, and this number grew to 281 million in 2015. The projected population growth indicates a continued trend toward urbanization, wherein it is anticipated that 381 million individuals will reside in urban areas by 2030 and this number will further increase to 527 million by 2050. The urban population growth in the MENA region is relatively faster than its overall population growth. Urban residents tend to consume more food, energy, water, and land, resulting in changes in environmental quality that can negatively impact their health and life standards. Consequently, a growing number of MENA urban residents will be impacted by climate risks. Previous studies such as^{3,18,21,61–67} have demonstrated that the process of urbanization is connected with a rise in CO₂ emissions within certain nations or cohorts of nations.

EE-CO₂ emissions linkage

The conservation of energy during the services and commodities production is noticed as crucial in reducing CO₂ emissions and is attained by the fulfillment of EE scales.⁶⁸ Empirical studies conducted in different countries and regions have examined the efficacy of EE.^69–77 According to the findings of these studies, the implementation of EE measures reduces CO₂ emissions.

Economic growth-CO₂ emissions linkage

The nexus between economic growth and carbon dioxide emissions is critical to sustainable progress. As every economic activity requires energy, GDP has a substantial impact on environmental contamination.⁶⁸ EKC is a conceptual framework that describes the relation between GDP and the quality of environment. According to Kuznets⁷⁸ this relationship has an inverted U-shape, meaning that as GDP rises, so do CO₂ emissions up to a certain point. Beyond this threshold, continued economic expansion results in environmental improvement. Some researchers that have examined the EKC hypothesis in the last years are mentioned regarding the issue. For example, Sun, Li⁷⁹ observed a U-shaped long-run linkage between China's CO₂ emissions and, also, the inverted N-shaped EKC for India from 1978 to 2019 was validated by Bandyopadhyay and Rej.⁸⁰ In contrast to these studies, Rahman, Nepal⁸¹ has rejected the presence of the EKC hypothesis evidence in industrialized nations. Ahmed, Cary⁸² investigated the economic growth’ symmetric and asymmetric effects on Japan's CO₂ emissions and found that the growth of the economic led to a rise in environmental degradation.⁸³ found similar outcomes in Russia. Different Scientists also probed economic development impacts on CO₂ emissions, that is, the investigations by Kibria, Akhundjanov⁸⁴ for 151 global countries⁸⁵ for 22 African countries and⁸⁶ for France.

During our literature review, we did not encounter any studies conducted on the MENA countries that specifically investigate the combined influence of urbanization, RE, economic growth, and EE adoption. Additionally, studies on RE have yielded inconsistent findings. This research employs the innovative cross-sectional autoregressive distributed lag (CS-ARDL) model to provide novel evidence that has not been exhaustively explored in prior scholarly research. The CS-ARDL method offers several benefits, including the ability to manage cross-sectional dependence (CD), endogeneity, and serial correlation, as well as to incorporate variable characteristics and generate short- and long-run forecasts.^87,88 Furthermore, it is currently unknown whether any studies have explored the influence of RE on the quality of environment in the MENA region through the implementation of principal component analysis (PCA) in conjunction with other environmental variables. The key contributions of this study are the attention paid to these voids. Also included are recommendations for policy in MENA countries based on the data given, which should help those countries work toward the UN's sustainable development objectives.

Data and study methodology

Data

The study uses Stata 17 and XLSTAT software for the purpose of model estimation and testing. The utilization of the innovative CS-ARDL model in this research constitutes a noteworthy contribution to the existing body of literature. This model effectively addresses CD, endogeneity, and serial correlation, while also incorporating variable characteristics. Furthermore, it provides both short and long-run forecasts.^87,88 Further, the present research endeavors to address a lacuna in the scholarly discourse by examining the influence of RE on emissions in MENA countries through the utilization of PCA and other ecological factors that have not been taken into account in prior investigations. The study's research outcomes have generated policy suggestions for MENA nations to reach the sustainable development aims stipulated by the United Nations.

This research investigates the interrelationships between urbanization, RE, economic growth, EE, FF utilization, and CO₂ emissions in 18 MENA countries between 1990 and 2019 using annual data. In the present investigation, carbon emissions (CO₂) constitute the dependent variable, being defined as the overall quantity of carbon dioxide that is released as a result of energy consumption. The investigation examines a quantity of independent variables that include urbanization (URB), calculated as the ratio of urban inhabitants to the total populace; gross domestic product (GDP) per capita (in constant 2017 US dollars); FF energy consumption, covering the collective usage of coal, petroleum, oil, and natural gas as a percentage of overall energy usage; EE, quantified as GDP per unit of energy utilized; and RE consumption. The World Bank has supplied information regarding various factors such as CO₂ emissions, GDP per capita, EE, FFE, and urbanization. Additionally, data pertaining to the components of RE has been provided by the International Renewable Energy Agency.⁸⁹ The process of estimating and testing the model was carried out using Stata 17 and XLSTAT.

In consideration of the data diversity of RE proxies for the MENA countries, this study utilized four RE indicators, namely solar, wind, hydropower, and biogas, to create an RE index for each country. PCA technique was then used to merge the four variables mentioned above into a single index instead of using them separately. PCA is a method that facilitates the transformation of data from a feature space with high dimensions to a feature space with low dimensions while preserving a notable proportion of the variability inherent in the original dataset. This enables the reduction of variables.² The RE index derived through PCA offers a comprehensive comprehension of the total influence of RE on carbon dioxide release in the MENA area. The corresponding author can provide the PCA results if needed.

Methodology

Econometric methodology

In this study, the econometric estimation process consists of five steps. In the initial phase to evaluate the cross-sectional independence of the data, the Pesaran⁹⁰ CD test was employed. The subsequent step endeavors to ascertain the slope coefficients homogeneity of the co-integration equation. Based on the given underlying premise of CD, the third phase of research entails the utilization of the CIPS and CADF unit root tests. The execution of panel co-integration tests on the variables within the MENA nations to scrutinize their long-run co-integration was performed in the fourth step. The fifth and final step involves the utilization of the CS-ARDL technique to compute the short and long-run coefficients, alongside the adjusted speed coefficient in the ECM model.

CD test

The determination of cross-sectional independence or dependence is an indispensable prerequisite to conducting the panel unit root analysis. CD can influence the actual parameter values of coefficient estimates. CD arising from unobserved common factors drastically reduces data efficiency advantages when ignored.⁹¹ Given the utilization of panel data in the present study, the CD must be considered to provide a reliable estimate of the coefficients.

Following Chudik and Pesaran,⁹² the panel data sample was exposed to CD and LM-CD tests to evaluate the possibility of rejecting the null hypothesis. Following is a concise overview of the conventional formula employed in cross-sectional dependency assessments.

CSD = \sqrt{\frac{2 T}{N (N - 1)} (\sum_{i = 1}^{N - 1} \sum_{K = i + 1}^{N} \hat{Correlatio n_{i, t}})}

(1)

Slope homogeneity test

In the econometrics of panel data, the heterogeneity in slopes is a critical issue since the weights of all countries differ. The present investigation aims to tackle this matter using the Hashem Pesaran and Yamagata⁹³ slope heterogeneity (SH) test, which takes into consideration the interdependence among the variables in the dataset and strengthens the reliability of the findings. Furthermore, Hashem Pesaran and Yamagata⁹³ test yields reliable outcomes under the condition where N ˂ T.

The equation for this test, which examines how the weighted slopes of all countries are dispersed, is as follows:

\hat{Δ} = \sqrt{N} (\frac{N^{- 1} \bar{S} - k}{\sqrt{2 k}}) \sim X_{k}^{2}

(2)

{\hat{Δ}}_{adj} = \sqrt{N} (\frac{N^{- 1} \bar{S} - k}{v (T, K)}) \sim N (0, 1)

(3)

In the given context, the symbol N represents the number of cross-sectional units, whereas the symbol T represents the dimension of the time series. The symbol k, denotes the independent variables. If the test's p-value exceeds 5%, the zero hypothesis is deemed accepted at the 5% significance level and the co-integrating coefficients are considered to be homogeneous. The statistical estimators

\hat{Δ}

and

{\hat{Δ}}_{adj}

are appropriate for use in the context of large and small sample sizes, respectively. It is worth noting that

{\hat{Δ}}_{adj}

is a version

\hat{Δ}

that has been adjusted for mean-variance bias.

Unit root tests

Variable stationarity assessment is a pivotal stage in any econometric investigation. The initial generation unit root test exhibits limited statistical power and lacks efficacy when employed on panel data that already manifests cross-sectional dependence concerns.^94,95 This study intends to examine the hypothesis of CD through the utilization of CIPS and CADF second-generation unit root tests, in order to evaluate the degree of integration among the variables. The utilization of panel unit root tests is crucial in panel co-integration models as they offer reliable estimators, which is in contrast to the inconsistent estimators provided by first-generation methods. When the integration degree was at I(1), it implies that the complete dataset exhibits a unit root issue at the I(0) and attains stationarity upon taking the variable's first difference. Therefore, it can be deduced that a plausible equilibrium relationship may exist over a long run within all variables encompassed in the dataset. The mathematical expressions of CIPS and CADF tests are presented below:

CIPS = \frac{1}{N} \sum_{i = 1}^{N} t_{i} (N, T)

(4)

Δ Y_{i t} = β_{i} + θ_{i} Y_{i, t - 1} + γ_{i} {\bar{Y}}_{t - 1} + \sum_{j = 0}^{P} γ_{i j} {\bar{Y}}_{t - 1} + \sum_{j = 1}^{P} ϑ_{i j} Δ Y_{i, t - 1} + ε_{i t}

(5)

For all cross-sectional, variables

{\bar{Y}}_{t - 1}

and

Δ Y_{i, t - 1}

represent the series lagged average and first difference, respectively.

Panel co-integration test

In instances where theories strongly suggest co-integration, first-generation techniques have been observed to embrace the zero hypothesis of the absence of co-integration. To address this issue, Westerlund⁹⁶ introduced an innovative method for panel co-integration testing that prioritizes structural dynamics over residual dynamics. The present investigation employs the bootstrap panel co-integration method introduced by Westerlund⁹⁶ to analyze the enduring relationship between the observed variables. The author elucidates that the results of the novel examination exhibit a considerable level of precision and possess greater efficacy compared with the residual-based tests introduced by Pedroni.⁹⁷ Furthermore, the desired methodology allows for the investigation of the gradients of diversity and auto-correlated residuals. Moreover, this methodology is suitable for investigating the presence of structural discontinuities in panel datasets.

Westerlund⁹⁶ introduced G_a, G_t, P_a, and P_t as four test statistics derived from the error correction (EC) model. The distribution of these statistics is typically Gaussian, with Gt and Pt being calculated based on the standard error (SE) parameters of the ECM. Moreover, the computation of G_a and P_a is contingent upon the deployment of the SE derived from Newey and West⁹⁸ suitably adjusted to account for the presence of autocorrelations and heteroscedasticity. In order to conduct the analysis, it is postulated that each variable exhibits first difference stationarity, denoted as I(1). The evaluation in question aims to determine the lack of co-integration by scrutinizing the existence of EC for both the entire group and individual cross-sections of the panel. The findings suggest that the aforementioned assessments exhibit limited normal distributions and possess greater dependability in relation to their consistency. The statistical measures for group tests denoted as (G_t and G_a), and for cross-sections tests, denoted as (P_t and P_a), can be mathematically shown in the following manner:

G_{t} = \frac{1}{N} \sum_{i = 1}^{N} \frac{\propto_{i}}{S E (\propto_{I})}

(6)

G_{a} = \frac{1}{N} \sum_{i = 1}^{N} \frac{T \propto_{i}}{\propto_{I} (1)}

P_{t} = \frac{\propto}{S E (\propto)}

P_{a} = T \propto

The calculation of EC, represented by the symbol ά, involves the incorporation of T and P_a values into the final equation.

Cross-sectional autoregressive distributed lag

In this work, the second-generation CS-ARDL⁹² technique is applied in order to estimate the long- and short-run coefficients after the existence of a long-run connection has been demonstrated using the panel co-integration test of Westerlund.⁹⁶ This method is preferred due to its superior efficiency and robustness compared to other conventional estimation techniques in several aspects. The CS-ARDL estimation technique's ability to effectively deal with challenges related to CD and SH distinguishes it from alternative methodologies such as augmented mean group (AMG), common correlated effect mean group (CCEMG), mean group (MG), and pooling mean group (PMG) approaches. Moreover, this examination necessitates significantly lesser time and exertion.⁹⁹ Also, it can handle mixed orders of integration or non-stationarity.¹⁰⁰ Finally, it can effectively resolution of the endogeneity predicament. The equation form that is commonly used for the CS-ARDL model is presented below.

LC O_{2} = α_{i t} + \sum_{j = 1}^{P} β_{i t} LC O_{2 i, t - j} + \sum_{j = 0}^{P} γ_{i t} X_{t - j} + \sum_{j = 0}^{q} δ {\bar{Y}}_{t - j} + ε_{i t}

(7)

where

{\bar{Y}}_{t} = (Δ L \bar{CO 2_{t}}, \bar{X_{t}^{'}})^{/}

displays cross-section averages and all independent variables are shown by

X_{t - j} = (lnEE, lnFFE, lnGDP, lnURB, lnRE, lnSGDP)

Result and discussion

Result of CD tests

Conducting a CD test on a dataset is imperative prior to estimating panel data in order to prevent model misspecification, which can lead to unreliable test results, biased and inconsistent coefficient estimates, and inefficiencies. The present investigation employs the four CD tests to attain reliable outcomes. The outcomes of the CD examination are presented in Table 1.

Table 1.

Results of Cross-sectional dependence(CD) tests.

Variables	Breusch–Pagan LM	Pesaran CD	Bias-corrected scaled LM	Pesaran Scaled LM
lnCO₂	1067.631^a	3.204^a	60.748^a	61.161^a
lnEE	733.922^a	−2.770^b	39.207^a	39.628^a
lnFFE	453.407^a	1.772^c	21.100^a	21.521^a
lnGDP	833.681^a	7.917^a	45.646^a	46.067^a
lnURB	1856.869^a	35.443^a	111.693^a	112.114^a
lnRE	1658.667^a	39.956^a	98.899^a	99.320^a
lnSGDP	834.157^a	7.886^a	45.677^a	46.098^a

Notes: ^a,b,c, and ^ns indicate significance at 1%, 5%, 10% levels, and insignificance, respectively.

According to the outcomes of the four tests, it can be derived that the zero hypothesis (H₀), which assumes the lack of CD for all variables except the lnEE and lnFFE variables, has been rejected at 1% significant level. The Pesaran CD test results suggest that the H₀ for the lnEE variable is rejected at the 5% level of significance. Additionally, the other three tests indicate rejection of the H₀ at a 1% level of significance. Furthermore, it is evident that the H₀ is rejected at a 10% level of significance for the lnFFE variable, as ascertained by the Pesaran CD test. The outcomes of the three other tests demonstrate that the alternative hypothesis (H₁) is accepted at 1%. The existence of CD among involved variables suggests that the environmental policies that have been put in place by the countries being examined are mutually reliant. Hence, in the event of an unforeseen disturbance in any of the MENA nations, the neighboring countries are also subject to its impact. Thus, it is apparent that the adoption of shared environmental policies can proficiently mitigate the release of CO₂ among the countries under analysis. The findings suggest that the consideration of CD for variables is imperative in the subsequent phases of the analysis.

Result of slope homogeneity

Table 2 displays the outcomes of the slopes homogeneity test. Assuming a continuous uniform distribution of slope values, we will conduct the test. Table 2 displays the results, indicating that delta and delta adjusted exhibit statistical significance at the significance level of 1%. The model is focused on heterogeneity, leading to the rejection of the H₀ of homogeneity for slope quantities.

Table 2.

Results of slope homogeneity tests.

Slope homogeneity tests	Delta	p-value
	12.206^a	0.000
adj	14.254^a	0.000

Notes: ^a,b,c, and ^ns indicate significance at 1%, 5%, 10% level and insignificance, respectively.

Result of correlation matrix and unit root test

Before conducting the estimation process, it is essential to ascertain the correlation matrix among the variables. So, Pearson's correlation method was utilized to ascertain the correlation between every pair of variables. The table indicates a positive correlation between environmental degradation and GDP, rate of urbanization, population, and FF. The correlation between EE, RE, and environmental pollution is the opposite (Table 3).

Table 3.

The results of correlation test.

	CO₂	EE	GDP	URB	FFE	RE
CO₂	1
EE	−0.66296	1
GDP	0.953317	−0.4272	1
URB	0.724098	−0.36743	0.706842	1
FFE	0.436136	−0.45736	0.34591	0.273178	1
RE	−0.043303	−0.03044	0.025932	0.108975	−0.06498	1

Typically, econometric techniques assume that the variables that are being investigated are stationary. Because estimates with non-stationary variables may be erroneous, citing such estimates will lead to misleading results.¹⁰¹ Before using this data, it is crucial to specify whether it is stationary or non-stationary (Table 4).

Table 4.

Results of CIPS and CADF unit root tests.

Tests	CIPS		CADF
Variables	Level	First-difference	Level	First-difference
lnCO₂	−2.0188	−3.3400^a	−1.8509	−4.4232^a
lnEE	−1.7415	−4.6623^a	−1.5604	−4.4623^a
lnFFE	−1.9985	−4.1665^a	−1.2734	−3.8634^a
lnGDP	−1.8231	−2.9518^a	−1.5258	−4.2314^a
lnURB	−2.1823	−2.4892^a	−1.5462	−4.6228^a
lnRE	−1.8762	−3.7302^a	−1.4782	−4.1293^a
lnSGDP	−1.9234	−2.3987^a	−1.7528	−3.9879^a

Notes: ^a,b,c, and ^ns indicate significance at 1%, 5%, 10% levels, and insignificance, respectively.

Table 4 displayed the findings of CIPS and CADF tests. All study variables demonstrate the existence of unit root issues at the intercept level. Also, this issue disappears when the first differences between variables are taken into account. Thus, all variables exhibit stationarity when differenced once.

Result of cross-sectional and co-integration

Notwithstanding the existence of unit root issue in the variables discussed earlier, aggregating these variables across time may yield a significant cointegration association in the long run. This study has utilized the Westerlund and Edgerton¹⁰² approach to investigate the enduring association between the variables under consideration. In addition to serving as a proficient method for assessing the presence of CD, this approach facilitates the examination of the variances in heterogeneity and errors that are serially correlated. Moreover, this methodology is suitable for investigating the existence of structural discontinuities in panel data.

Table 5 indicates that the zero hypothesis, which posits the nonexistence of a co-integration relation among variables across all models, has been refuted. The findings indicate that there exists a sustained association among EE, FF, GDP, URB, RE, SGDP, and CO₂ emissions.

Table 5.

Results of Westerlund and Edgerton¹⁰² bootstrap panel co-integration.

Statistics	Value	Z value	p value
G_t	−4.154	−6.772	0.000
G_a	−16.735	−1.379	0.084
P_t	−15.901	−6.199	0.000
P_a	−16.221	−2.838	0.002

Data source: research finding.

Result of CS-ARDL

The obtained results from the long and short-run estimation for EE, GDP, urbanization rate, FF, RE index, and the SGDP on carbon emissions have been documented in Table 6, utilizing the CS-ARDL estimator.

Table 6.

The short- and long-run CS-ARDL estimator results.

Variable	Coefficients	Std. err.	z-statistics	p > $\| z \|$
Long-run equation
EE	−0.6283^a	0.0436	−14.40	0.0000
GDP	0.2746^b	0.0924	2.97	0.0126
URB	0.5538^b	0.2487	2.22	0.0295
FF	1.2453^a	0.2173	5.90	0.0000
RE	−0.0130^b	0.0041	−3.17	0.0021
SGDP	−0.0182^ns	0.0312	−0.58	0.5602
Short-run equation
EC(−1)	−0.7832^a	0.1250	−6.26	0.0000
D(EE)	−0.5603^a	0.0691	−8.11	0.0000
D(GDP)	−34.2688^ns	21.500	−1.62	0.1064
D(URB)	7.4724^ns	9.3673	0.80	0.4256
D(FF)	1.0506^ns	1.6295	0.62	0.5192
D(RE)	−0.0132^ns	0.0097	−1.36	0.1735
D(SGDP)	1.6351^c	0.9182	1.78	0.0753
CONS	−0.0251^ns	0.0242	−1.04	0.3001

Notes: ^a,b,c, and ^ns indicate significance at 1%, 5%, 10% levels, and insignificance, respectively.

The anticipated long and short-run coefficients of the EE exhibit a relatively modest influence on carbon emissions. The findings indicate that an increase of 1% in EE caused a statistically significant reduction in CO2 emissions by 0.6283% and 0.5603% in the long- and short-run, respectively, in the MENA countries. These findings suggest a positive relationship between EE and CO₂ abatement, underpinning the significance of promoting EE in the MENA region for attenuating the adverse effects of CO₂ emissions on the environment. Also, results demonstrate that the procedures, technology, and instruments employed in each given endeavor are mirrored in the decrease of carbon emissions. These outcomes align with Lei⁷⁷ and Xia,¹⁰³ who acknowledged in their study that EE enhanced the environmental standard in both the short- and long-run.

Moreover, our analysis yields GDP coefficient estimations that exhibit both statistical significance and a positive directionality. However, in the nations investigated, the impact was minor in the short-run. In such a circumstance, it is possible to argue that GDP is solely connected to CO₂ release in the long run. In other words, carbon dioxide emissions exhibit an increase by 0.2746% for a 1% rise in GDP. Furthermore, in terms of GDP square (SGDP), the elasticity of CO₂ emissions over the long-run shows a negative value and is statistically negligible. In contrast to GDP, it can be noted that SGDP did not curtail carbon dioxide over the long-run. The computed GDP and SGDP coefficients show that the linkage between economic prosperity and carbon dioxide release is linear in MENA nations. Consequently, the escalation of per capita GDP impacts the deterioration of the environment in the examined countries.

The long-run coefficient for urbanization (URB) exhibits a substantial and positive relationship, while the short-run coefficient, in contrast, demonstrates a positive but insignificant association. It demonstrates that, in the long run, URB correlates to increasing CO₂ emissions and environmental deterioration. However, this result is supported by Sun¹⁰⁴ for the MENA region. Now moving toward the coefficients elasticity, the elasticity of CO₂ emissions concerning URB is 0.5538. This suggests that an increase of 1% in the URB rate will lead to a rise in CO₂ emissions by 0.5538%. The urban population in the MENA region has witnessed an increase from 46% in 1974 to 66% in 2021. The exponential growth of urbanization has resulted in an augmented requirement for commercial infrastructure, residential units, transportation systems, mobility services, and electrical appliances. Ultimately, caused FFs to reign transportation energy consumption. Overall, massive urbanization increased energy use, traffic congestion, generation of waste, forest deterioration, and alteration in land use within the MENA region. So, the aforementioned factors have notably contributed to the escalation of CO₂ emissions.

The coefficient of FF consumption in MENA countries exhibits a positive and statistically significant relationship with environmental degradation, indicating that a rise in FF usage is related to a rise in CO₂ emissions. A marginal rise of 1% in the utilization of FF leads to an upsurge of 1.2453% in the emission of carbon dioxide, indicating a robust nexus between these two variables. The discovered results are in alignment with the investigations carried out by Abdallh and Abugamos¹⁸ and Li and Haneklaus.¹⁰⁵ These studies suggest that the utilization of FF constitutes a considerable determinant in the deterioration of the environment in the MENA region and China, respectively. The finding is to be expected in a locality where FF accounts for 97% of the overall energy consumption. A considerable proportion of the world's confirmed reserves of oil and natural gas, in excess of fifty percent, are held by the nations mentioned above. To facilitate economic development and urbanization, there is a growing demand for increased consumption of FF. The countries in the MENA region allocate substantial funds toward fuel subsidies for diverse economic sectors.

The empirical finding indicates an inverse nexus between the RE index and carbon dioxide. To provide greater specificity, it can be stated that a 1% increase in RE utilization yields a long-run reduction of 0.0130% in CO2 emissions. For short-run, this assessment indicates that the growth of the RE sector in the MENA is negative and inconsequential. This research shows that the MENA region's environmental quality will improve over time if these nations increase their usage of various RE sources (namely solar, wind, biomass, geothermal, and hydropower). This current finding exhibit resemblances to previous research works conducted by Hussain and Rehman¹⁰⁶ and Liu,²² indicating that the proliferation of RE sources in Pakistan and Northeast Asia contributes to the enhancement of environmental quality.

According to the data presented in Table 6, it was determined that the EC coefficient exhibits a statistically significant value of −0.7832 at the 1% significance level. This variable represents the rate at which shocks adapt to long-run equilibrium in the short-run. The aforementioned coefficient in this research shows that 78% of shocks input was adjusted in each session, indicating a speedier adjustment process.

Conclusion and policy implications

The purpose of the present study is to survey the linkage between EE, economic prosperity, urbanization, FF consumption, RE, and ecological footprint in the MENA region over the period of 1990–2019 in light of the critical environmental challenge they face. Previous studies have, to our knowledge, discussed the determinants of CO₂ emissions, but this study takes a novel approach by developing a RE index using PCA. Specifically, the PCA is used to combine the four variables of solar, wind, hydropower, and biogas into a merged index, allowing for a further detailed assessment of the total effect of RE on the MENA region’ CO₂ emissions. The analysis applied the CS-ARDL technique to estimate the linkage between these variables, thereby providing valuable insights for policymakers and other stakeholders who seek to promote sustainable development in the MENA countries.

The negative influence of EE on carbon dioxide (−0.6283) indicates that MENA nations must adopt more efficient measures as part of their “energy efficiency” programs. Although MENA nations did not sign the Kyoto Protocol since they are emerging economies, it is vital for these countries to accept the difficulties of limiting CO₂ emissions and energy consumption and to implement creative strategies to promote EE and prevent environmental damage. These efforts would imply increase energy security, increased economic growth, and the expansion and transformation of energy-intensive businesses like the automobile industry. To accomplish energy savings, EE must also improve via technical innovation and increased consumer awareness.

Every nation's first priority should be stimulating its economy to expand. The Gross Domestic Product is a pivotal element component in generating carbon dioxide emissions and ecological degradation since all economic activity requires energy to generate products and services. In accordance with the EKC hypothesis, GDP results in higher CO₂ emissions up to a specified threshold, and then the link between economic development and environmental harm flattens out. If GDP growth continues over this threshold, conditions will start to improve. Our long-run findings show that the Gross Domestic Product shows a positive impact (0.2746) on CO₂ emissions in MENA nations, making it the single most important measure of economic development. The negative and statistically negligible impact of the squared GDP term (−0.0182) suggests that economic development and environment degradation display a linear connection, suggesting that in the long run, the EKC hypothesis is not supported.

Concurring to the data, urbanization incorporates a statistically significant positive long-run coefficient (0.5538) while demonstrating a positive, yet statistically insignificant coefficient (7.4724) the in short-run. It shows that urbanization is responsible for increased CO₂ emissions and environmental deterioration. This might be because urbanization is associated with a rise in economic activity, which in turn leads to higher income and economic development, both of which boost the demand for energy-intensive goods (cars, factories, etc.). Carbon emissions are increased as a result of using these items. Furthermore, Moreover, it is noteworthy that the advantageous effects of urbanization on carbon dioxide (CO2) release underscore the necessity of preventing over-urbanization and ensuring adequate provision of resources to rural communities to mitigate the need for migration to urban areas. As well as public transit should be strengthened to discourage the purchase of private automobiles and so cut down on the excessive carbon emissions from cars caused by the strong demand for private transportation. These novel initiatives have the potential to slow the rate of urban energy utilization and cut carbon dioxide emissions throughout the urbanization processes of this nation. The negative consequences of urbanization on the environment will worsen if they are not properly handled. These suggestions, if followed, would lessen the negative long-run consequences on environmental quality and enable MENA nations to move closer to environmental sustainability.

The coefficient denoting the consumption of FF exhibits a positive and noteworthy value (1.2453) that highlights the direct impact of FF use on CO₂ emissions within MENA nations. The prevalence of inexpensive FF in the MENA region has led to excessive consumption of these fuels, necessitating a transformation of energy policies from FF to cleaner energy solutions. One effective approach is the implementation of a tax on polluting industries that rely on FF. This initiative may encourage the promotion of RE adoption. To create a sustainable environment in the midst of economic expansion in MENA nations, a gradual transition to RE is essential. Investing heavily in RE and other eco-friendly technology will help reduce carbon dioxide emissions and ensure a sustainable environment. Policymakers face a significant challenge to control and reduce CO₂ emissions from nonrenewable energy (NRE) usage through RE consumption. This investigation found an opposite dynamic linkage (−0.0130) between carbon dioxide emissions and the RE index, indicating the need for MENA country administrators to subsidize RE generation, including solar, wind, hydropower, and biogas. The government should allocate funds more effectively and prioritize clean and RE generation to reach efficient and sustainable economic growth. Additionally, promoting the use of electric vehicles and investing in the necessary infrastructure can be beneficial.¹⁰⁷

Highlights

Focus on MENA countries as the largest CO₂ emissions in the globe

Urbanization rate and nonrenewable energy positively and significantly influence the CO₂ emissions

EE and RE consumption can lead to less CO₂ emissions

EKC was tested in MENA region

CS-ARDL and PCA analysis techniques were used

Footnotes

Author contributions

The authors hereby declare their respective contributions to this paper as follows: AM and SSY are accountable for the idea and design of the manuscript, MHEN for the data collection process, and AM and SSY for the analysis and interpretation of results. The drafting of the MS has been primarily executed by SSY. The authors conducted a thorough review of the findings and provided their final approval of the MS.

Data availability

Not available.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Saeid Satari Yuzbashkandi

Saeid Satari Yuzbashkandi holds a PhD in agricultural economics. His research interests include “developing economic models to value natural resources and environmental goods, assessing the economic impacts of climate change on various sectors of the economy, analyzing the nexus between economic growth and environmental quality, and developing optimal allocation strategies for water resources”.

Amir Mehrjoo studies agricultural economics for his PhD The influence of climate change on the economy is the focus of his study.

Mohammad Hadi Eskandari Nasab is an agricultural economics engineer who is extremely enthusiastic about researching “Environment problems in terms of Economy.”

References

Lin

Zhu

. The role of renewable energy technological innovation on climate change: empirical evidence from China. Sci Total Environ 2019; 659: 1505–1512.

Mehrjo

Satari Yuzbashkandi

Eskandari Nasab

, et al. Economic complexity, ICT, biomass energy consumption, and environmental degradation: evidence from Iran. Environ Sci Pollut Res 2022; 29: 69888–69902.

Ahmed

Wang

Mahmood

, et al. Does globalization increase the ecological footprint? Empirical evidence from Malaysia. Environ Sci Pollut Res 2019; 26: 18565–18582.

Ikram

Xia

Fareed

, et al. Exploring the nexus between economic complexity, economic growth and ecological footprint: contextual evidences from Japan. Sustainable Energy Technolo Assess 2021; 47: 101460.

Nathaniel

Khan

SAR

. The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries. J Cleaner Prod 2020; 272: 122709.

IEA (International Energy Agency). Global energy & CO₂ status report. 2022.

Saint Akadiri

Adewale Alola

Olasehinde-Williams

, et al. The role of electricity consumption, globalization and economic growth in carbon dioxide emissions and its implications for environmental sustainability targets. Sci Total Environ 2020; 708: 134653.

Apergis

Payne

. The oil curse, institutional quality, and growth in MENA countries: evidence from time-varying cointegration. Energy Econ 2014; 46: 1–9.

Baloch

Tan

Iqbal

, et al. Trilemma assessment of energy intensity, efficiency, and environmental index: evidence from BRICS countries. Environ Sci Pollut Res 2020; 27: 34337–34347.

10.

Asbahi

AAMHA

Gang

Iqbal

, et al. Novel approach of principal component analysis method to assess the national energy performance via energy trilemma index. Energy Rep 2019; 5: 704–713.

11.

Wodon

Liverani

Joseph

, et al. Climate change and migration: evidence from the Middle East and North Africa. Washington, DC: World Bank Publications, 2014.

12.

WDI. World bank indicators. 2022. Available at: https://data.worldbank.org/indicator

13.

Dudley

. BP statistical review of world energy. BP Statistical Review, London, UK. 2018; 6: 00116.

14.

Chentouf

Allouch

. Environmental energy security in the MENA region – an aggregated composite index. Environ Dev Sustainability 2021; 24: 10945–10974.

15.

Acheampong

. Economic growth, CO2 emissions and energy consumption: what causes what and where? Energy Econ 2018; 74: 677–692.

16.

Shahbaz

Sharma

Sinha

, et al. Analyzing nonlinear impact of economic growth drivers on CO2 emissions: designing an SDG framework for India. Energy Policy 2021; 148: 111965.

17.

Zhou

Poon

Yang

. China’s CO2 emission intensity and its drivers: an evolutionary geo-tree approach. Resour Conserv Recycl 2021; 171: 105630.

18.

Abdallh

Abugamos

. A semi-parametric panel data analysis on the urbanisation-carbon emissions nexus for the MENA countries. Renewable Sustainable Energy Rev 2017; 78: 1350–1356.

19.

Omri

Daly

Rault

, et al. Financial development, environmental quality, trade and economic growth: what causes what in MENA countries. Energy Econ 2015; 48: 242–252.

20.

Arouri

MEH

Ben Youssef

M’Henni

, et al. Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy 2012; 45: 342–349.

21.

Anwar

Sinha

Sharif

, et al. The nexus between urbanization, renewable energy consumption, financial development, and CO2 emissions: evidence from selected Asian countries. Environ Dev Sustainability 2022; 24: 6556–6576.

22.

Liu

Kong

Zhang

. Can urbanization, renewable energy, and economic growth make environment more eco-friendly in Northeast Asia? Renewable Energy 2021; 169: 23–33.

23.

Hoornweg

Bhada-Tata

Kennedy

. Environment: waste production must peak this century. Nature 2013; 502: 615–617.

24.

Ribeiro

Rybski

Kropp

. Effects of changing population or density on urban carbon dioxide emissions. Nat Commun 2019; 10: 3204.

25.

Plaza

. A blue energy option for the Mekong river basin. An international law analysis on Asian regional cooperation in pioneer osmotic power projects. Sustainable Energy Technol Assess 2018; 25: 75–98.

26.

Park

Rajah

. Asian development outlook 2017: Transcending the middle income challenge. Philippines: Asian Development Bank, 2017.

27.

Abbasi

Adedoyin

Abbas

, et al. The impact of energy depletion and renewable energy on CO2 emissions in Thailand: fresh evidence from the novel dynamic ARDL simulation. Renewable Energy 2021; 180: 1439–1450.

28.

Amirnejad

Mehrjo

Satari Yuzbashkandi

. Economic growth and air quality influences on energy sources depletion, forest sources and health in MENA. Environ Challenges 2021; 2: 100011.

29.

Ahmed

Adebayo

Udemba

, et al. Effects of economic complexity, economic growth, and renewable energy technology budgets on ecological footprint: the role of democratic accountability. Environ Sci Pollut Res 2022; 29: 24925–24940.

30.

Espoir

Mudiangombe Mudiangombe

Bannor

, et al. CO2 Emissions and economic growth: assessing the heterogeneous effects across climate regimes in Africa. Sci Total Environ 2022; 804: 150089.

31.

Mostafaeipour

Bidokhti

Fakhrzad

M-B

, et al. A new model for the use of renewable electricity to reduce carbon dioxide emissions. Energy 2022; 238: 121602.

32.

Sulaiman

Abdul-Rahim

Ofozor

. Does wood biomass energy use reduce CO2 emissions in European Union member countries? Evidence from 27 members. J Cleaner Prod 2020; 253: 119996.

33.

Kim

Choi

S-K

Seok

. Does biomass energy consumption reduce total energy CO2 emissions in the US? J Policy Model 2020; 42: 953–967.

34.

Chang

K-H

Lou

K-R

C-H

. Potential of bioenergy production from biomass wastes of rice paddies and forest sectors in Taiwan. J Cleaner Prod 2019; 206: 460–476.

35.

Kahia

Kadria

Ben Aissa

, et al. Modelling the treatment effect of renewable energy policies on economic growth: evaluation from MENA countries. J Cleaner Prod 2017; 149: 845–855.

36.

Biyik

Araz

Hepbasli

, et al. A key review of building integrated photovoltaic (BIPV) systems. Eng Sci Technol, Int J 2017; 20: 833–858.

37.

Islam

Pandey

Hasanuzzaman

, et al. Recent progresses and achievements in photovoltaic-phase change material technology: a review with special treatment on photovoltaic thermal-phase change material systems. Energy Convers Manage 2016; 126: 177–204.

38.

Kasaeian

Nouri

Ranjbaran

, et al. Solar collectors and photovoltaics as combined heat and power systems: a critical review. Energy Convers Manage 2018; 156: 688–705.

39.

Charfeddine

Kahia

. Impact of renewable energy consumption and financial development on CO2 emissions and economic growth in the MENA region: a panel vector autoregressive (PVAR) analysis. Renewable Energy 2019; 139: 198–213.

40.

Davies

Hodge

Schellekens

, et al. Developing renewable energy projects: A guide to achieving success in the Middle East. Egypt: EverSheds, Shahid Law Firm, Pwc, 2015.

41.

Menichetti

El Gharras

Duhamel

, et al. The MENA region in the global energy markets. MENARA 2018; 8: 75–119.

42.

Riahi

Zissler

Lins

, et al. MENA renewables status report. 2013.

43.

Sawin

Martinot

Sonntag-O’Brien

, et al. Renewables 2010 global status report. 2010.

44.

Leal-Arcas

Akondo

Alemany Rios

. Energy trade in the MENA region: looking beyond the Pan-Arab electricity market. J World Energy Law Bus 2017; 10: 520–549.

45.

Ang

Zhou

. Accounting frameworks for tracking energy efficiency trends. Energy Econ 2010; 32: 1209–1219.

46.

Akram

Chen

Khalid

, et al. Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: evidence from developing countries. J Cleaner Prod 2020; 247: 119122.

47.

Jalilvand

. Renewable energy for the Middle East and North Africa. Policies for a successful transition. Berlin: Friedrich-Ebert-Stiftung Department for Near/Middle East and North Africa, 2012.

48.

Shahbaz

Balsalobre-Lorente

Sinha

. Foreign direct investment–CO2 emissions nexus in Middle East and North African countries: importance of biomass energy consumption. J Cleaner Prod 2019; 217: 603–614.

49.

Adebayo

Rjoub

Akinsola

, et al. The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: new evidence from quantile-on-quantile regression approach. Environ Sci Pollut Res 2022; 29: 1875–1886.

50.

Haldar

Sethi

. Effect of institutional quality and renewable energy consumption on CO2 emissions − an empirical investigation for developing countries. Environ Sci Pollut Res 2021; 28: 15485–15503.

51.

Bekun

Alola

Sarkodie

. Toward a sustainable environment: nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci Total Environ 2019; 657: 1023–1029.

52.

Bilgili

Koçak

Bulut

. The dynamic impact of renewable energy consumption on CO2 emissions: a revisited environmental Kuznets curve approach. Renewable Sustainable Energy Rev 2016; 54: 838–845.

53.

Zoundi

. CO2 Emissions, renewable energy and the environmental Kuznets curve, a panel cointegration approach. Renewable Sustainable Energy Rev 2017; 72: 1067–1075.

54.

Xie

Fang

, et al. The role of renewable energy consumption and commercial services trade in carbon dioxide reduction: evidence from 25 developing countries. Appl Energy 2018; 211: 1229–1244.

55.

Khattak

Ahmad

Khan

, et al. Exploring the impact of innovation, renewable energy consumption, and income on CO2 emissions: new evidence from the BRICS economies. Environ Sci Pollut Res 2020; 27: 13866–13881.

56.

Nguyen

Kakinaka

. Renewable energy consumption, carbon emissions, and development stages: some evidence from panel cointegration analysis. Renewable Energy 2019; 132: 1049–1057.

57.

Saidi

Omri

. Reducing CO2 emissions in OECD countries: do renewable and nuclear energy matter? Prog Nucl Energy 2020; 126: 103425.

58.

Ben Jebli

Ben Youssef

. The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries. Ecol Indic 2017; 74: 295–301.

59.

Apergis

Payne

. Coal consumption and economic growth: evidence from a panel of OECD countries. Energy Policy 2010; 38: 1353–1359.

60.

Péridy

Brunetto

Ghoneim

. The economic costs of climate change in MENA countries: a micro-spatial quantitative assessment and a survey of policies. Femise, Marseille 2012.

61.

Al-Mulali

Ozturk

Lean

. The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. Nat Hazards 2015; 79: 621–644.

62.

Anser

Alharthi

Aziz

, et al. Impact of urbanization, economic growth, and population size on residential carbon emissions in the SAARC countries. Clean Technol Environ Policy 2020; 22: 923–936.

63.

Nathaniel

Anyanwu

Shah

. Renewable energy, urbanization, and ecological footprint in the Middle East and North Africa region. Environ Sci Pollut Res 2020; 27: 14601–14613.

64.

Aguir Bargaoui

Liouane

Nouri

. Environmental impact determinants: an empirical analysis based on the STIRPAT model. Proc-Soc Behav Sci 2014; 109: 449–458.

65.

Bello

Solarin

Yen

. The impact of electricity consumption on CO2 emission, carbon footprint, water footprint and ecological footprint: the role of hydropower in an emerging economy. J Environ Manag 2018; 219: 218–230.

66.

Magazzino

Cerulli

. The determinants of CO2 emissions in MENA countries: a responsiveness scores approach. Int J Sustainable Dev World Ecol 2019; 26: 522–534.

67.

Adebola Solarin

Al-Mulali

Ozturk

. Validating the environmental Kuznets curve hypothesis in India and China: the role of hydroelectricity consumption. Renewable Sustainable Energy Rev 2017; 80: 1578–1587.

68.

Ponce

Khan

SAR

. A causal link between renewable energy, energy efficiency, property rights, and CO2 emissions in developed countries: a road map for environmental sustainability. Environ Sci Pollut Res 2021; 28: 37804–37817.

69.

Malinauskaite

Jouhara

Egilegor

, et al. Energy efficiency in the industrial sector in the EU, Slovenia, and Spain. Energy 2020; 208: 118398.

70.

Román-Collado

Economidou

. The role of energy efficiency in assessing the progress towards the EU energy efficiency targets of 2020: evidence from the European productive sectors. Energy Policy 2021; 156: 112441.

71.

Chen

Cheng

. Effects of urbanization on energy efficiency in China: new evidence from short run and long run efficiency models. Energy Policy 2020; 147: 111858.

72.

Sun

Edziah

Sun

, et al. Institutional quality, green innovation and energy efficiency. Energy Policy 2019; 135: 111002.

73.

Marques

Fuinhas

Tomás

. Energy efficiency and sustainable growth in industrial sectors in European Union countries: a nonlinear ARDL approach. J Cleaner Prod 2019; 239: 118045.

74.

Vieira

NDB

Nogueira

LAH

Haddad

. An assessment of CO2 emissions avoided by energy-efficiency programs: a general methodology and a case study in Brazil. Energy 2018; 142: 702–715.

75.

Weng

Zhang

. The role of energy efficiency improvement and energy substitution in achieving China’s carbon intensity target. Energy Procedia 2017; 142: 2786–2790.

76.

Yushchenko

Patel

. Contributing to a green energy economy? A macroeconomic analysis of an energy efficiency program operated by a Swiss utility. Appl Energy 2016; 179: 1304–1320.

77.

Lei

Xie

Hafeez

, et al. Assessing the dynamic linkage between energy efficiency, renewable energy consumption, and CO2 emissions in China. Environ Sci Pollut Res 2022; 29: 19540–19552.

78.

Kuznets

. Economic growth and income inequality. Am Econ Rev 1955; 45: 1–28.

79.

Sun

Zhang

, et al. A study on China’s economic growth, green energy technology, and carbon emissions based on the Kuznets curve (EKC). Environ Sci Pollut Res 2021; 28: 7200–7211.

80.

Bandyopadhyay

Rej

. Can nuclear energy fuel an environmentally sustainable economic growth? Revisiting the EKC hypothesis for India. Environ Sci Pollut Res 2021; 28: 63065–63086.

81.

Rahman

Nepal

Alam

. Impacts of human capital, exports, economic growth and energy consumption on CO2 emissions of a cross-sectionally dependent panel: evidence from the newly industrialized countries (NICs). Environ Sci Policy 2021; 121: 24–36.

82.

Ahmed

Cary

Ali

, et al. Moving toward a green revolution in Japan: symmetric and asymmetric relationships among clean energy technology development investments, economic growth, and CO2 emissions. Energy & Environment 2021; 33: 1417–1440.

83.

Kanat

Yan

Asghar

, et al. Do natural gas, oil, and coal consumption ameliorate environmental quality? Empirical evidence from Russia. Environ Sci Pollut Res 2022; 29: 4540–4556.

84.

Kibria

Akhundjanov

Oladi

. Fossil fuel share in the energy mix and economic growth. Int Rev Econ Finance 2019; 59: 253–264.

85.

Mensah

Sun

Gao

, et al. Analysis on the nexus of economic growth, fossil fuel energy consumption, CO2 emissions and oil price in Africa based on a PMG panel ARDL approach. J Cleaner Prod 2019; 228: 161–174.

86.

Mohamed

Ben Jebli

Ben Youssef

. Renewable and fossil energy, terrorism, economic growth, and trade: evidence from France. Renewable Energy 2019; 139: 459–467.

87.

Usman

Ozturk

Naqvi

SMMA

, et al. Revealing the nexus between nuclear energy and ecological footprint in STIRPAT model of advanced economies: fresh evidence from novel CS-ARDL model. Prog Nucl Energy 2022; 148: 104220.

88.

Chandra Voumik

Sultana

. Impact of urbanization, industrialization, electrification and renewable energy on the environment in BRICS: fresh evidence from novel CS-ARDL model. Heliyon 2022; 8: e11457.

89.

IRENA. International Renewable Energy Agency. Abu Dhabi: Masdar, 2022.

90.

Pesaran

. General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics 0435, Faculty of Economics, University of Cambridge, 2004. DOI: https://doi.org/10.17863/CAM.5113.

91.

Phillips

PCB

Sul

. Dynamic panel estimation and homogeneity testing under cross section dependence. Econom J 2003; 6: 217–259.

92.

Chudik

Pesaran

. Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J Econom 2015; 188: 393–420.

93.

Hashem Pesaran

Yamagata

. Testing slope homogeneity in large panels. J Econom 2008; 142: 50–93.

94.

Paramati

Ummalla

Apergis

. The effect of foreign direct investment and stock market growth on clean energy use across a panel of emerging market economies. Energy Econ 2016; 56: 29–41.

95.

Bhattacharya

Paramati

Ozturk

, et al. The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Appl Energy 2016; 162: 733–741.

96.

Westerlund

. Testing for error correction in panel data*. Oxf Bull Econ Stat 2007; 69: 709–748.

97.

Pedroni

. Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econom Theory 2004 20: 597–625. DOI: 10.1017/S0266466604203073

98.

Newey

West

. Automatic lag selection in covariance matrix estimation. Rev Econ Stud 1994; 61: 631–653.

99.

Wang

Zhu

, et al. Unveiling the driving factors of carbon emissions from industrial resource allocation in China: a spatial econometric perspective. Energy Policy 2021; 158: 112557.

100.

Chudik

Mohaddes

Pesaran

, et al.

Is there a debt-threshold effect on output growth?

Rev Econ Stat 2017; 99: 135–150.

101.

Baltagi

. Econometric analysis of panel data. Midtown Manhattan/ New York city/ USA: Springer Cham, 2008.

102.

Westerlund

Edgerton

. A simple test for cointegration in dependent panels with structural breaks*. Oxf Bull Econ Stat 2008; 70: 665–704.

103.

Xia

Zhang

Cai

, et al. Identification of key industries of industrial sector with energy-related CO2 emissions and analysis of their potential for energy conservation and emission reduction in Xinjiang, China. Sci Total Environ 2020; 708: 134587.

104.

Sun

Andlib

, et al. How do renewable energy and urbanization cause carbon emissions? Evidence from advanced panel estimation techniques. Renewable Energy 2022; 185: 996–1005.

105.

Haneklaus

. The role of renewable energy, fossil fuel consumption, urbanization and economic growth on CO2 emissions in China. Energy Rep 2021; 7: 783–791.

106.

Hussain

Rehman

. Exploring the dynamic interaction of CO2 emission on population growth, foreign investment, and renewable energy by employing ARDL bounds testing approach. Environ Sci Pollut Res 2021; 28: 39387–39397.

107.

WUP. World urbanization prospects. 2018.

Exploring the dynamic nexus between urbanization,energy efficiency,renewable energies,economic growth,with ecological footprint: A panel cross-sectional autoregressive distributed lag evidence along Middle East and North Africa countries

Abstract

Keywords

Introduction

Literature review

CO2 emissions and RE

Urbanization-CO2 emissions linkage

EE-CO2 emissions linkage

Economic growth-CO2 emissions linkage

Data and study methodology

Data

Methodology

Econometric methodology

CD test

Slope homogeneity test

Unit root tests

Panel co-integration test

Cross-sectional autoregressive distributed lag

Result and discussion

Result of CD tests

Result of slope homogeneity

Result of correlation matrix and unit root test

Result of cross-sectional and co-integration

Result of CS-ARDL

Conclusion and policy implications

Highlights

Footnotes

Author contributions

Data availability

Consent for publication

Ethics approval and consent to participate

Declaration of conflicting interests

Funding

ORCID iD

References

CO₂ emissions and RE

Urbanization-CO₂ emissions linkage

EE-CO₂ emissions linkage

Economic growth-CO₂ emissions linkage