Dynamic Influence of Urbanization on Inclusive Green Growth in Belt and Road Countries: The Moderating Role of Governance

Abstract

The strategic objective of this study was to determine the influence of urbanization on inclusive green growth (IGG) with the moderating impact of governance while considering household consumption, exchange rate, per capita income, and the unemployment rate as control variables. The data set consisted of the years 2004–2019 for 64 countries along the Belt and Road (BRI) region. The outcomes of the two-step system of generalized method of moments (GMM) robust with D-K regression methodology confirmed the significantly positive dynamic nature of inclusive green growth. Urbanization showed a significant but negative influence on inclusive green growth, indicating environmental degradation due to unplanned urban growth. The moderating role of governance also depicted a significant negative impact, emphasizing the need for a transparent governance framework for BRI countries to utilize resources effectively. Moreover, the interaction term of urbanization and governance showed a significantly positive improvement toward inclusive green growth. Besides this, per capita income played a significantly positive role, while unemployment and household consumption had a negative but significant influence on inclusive green growth. Further, the exchange rate impacted insignificantly on inclusive green growth. Hence, it is evident that urbanization, good governance, and employment opportunities are required to promote sustainable green growth in BRI countries. The research identifies the factors that are required for sustainability/IGG.

1. Introduction

After the revolution of science and technology, the rate of urbanization is increasing as 70% of worldwide GDP (gross domestic product) is generated by urban areas [1]. Urbanization provides the opportunity for a job, marketplace competition, diversity, and proximity [2]. Urbanization is a socioeconomic phenomenon as it brings modernization and economic growth. The urbanization level signifies the social progress of the country. As per the 2018 Revision of World Urbanization Prospects, the rate of urbanization was approximately 55% in 2018, and it will reach 68% in 2050. In total, 90% growth will be observed in Africa and Asia, particularly in China and India. Urban area expanded from 0.23% to 0.53% from 1992 to 2013 [3]. Urbanization brings multiple benefits for human beings but has exploited natural resources that have deteriorated the environment. It is hurting biodiversity and producing land, water, and environmental pollution. Urbanization has created a series of issues, such as traffic jams, shortage of resources, and population concentration. Urban areas are contributing to greenhouse gas (GHG) emissions, almost 70% of total emissions [1]. Built-up areas and the growing urban population are increasing the pressure on the ecological system. Thus, it has become urgent for many economies to control the negative impacts of urbanization on the ecological environment. This deterioration of the environment threatens the living space of all humanity. Green growth aims at the common development of economic, social, and ecological systems [3]. Therefore, the concept of inclusive green growth (IGG) is very important for sustainable urban growth.

The perception of IGG (inclusive green growth) was discussed in British environmental economist Pearce’s Blueprint for the first time in 1989. It focuses on accomplishing a sustainable society and green economy from an environmental economics viewpoint. The UN (United Nations), the WB (World Bank), the OECD, as well as a few other international organizations, have promoted the concept of “IGG” globally because it is closely linked to sustainability. The OECD officially described IGG in 2009 as pursuing economic development without harm to biodiversity, climate change, and unverifiable usage of natural resources. The UNEP integrated social equity beyond the pursuit of economic growth in 2011, indicating green growth as the development of human welfare and social justice although predominantly decreasing environmental threats and ecological scarcity. The WB restated inclusive green growth in 2012 as progress that effectually utilizes natural resources to decrease pollution and environmental influences and enhance the public’s resilience to natural disasters. The OECD also determined the aim of national programs and policies of IGG in 2012. The purpose was to encourage the consumers and businesses to initiate environment-friendly activities; offer incentives and support to ecological innovations; and facilitate the equitable and smooth reallocation of jobs, technologies, and capital. The ultimate goal of all these interchangeable definitions and concepts of international organizations is to achieve inclusive green growth or sustainable development [4].

Inclusive green growth is a well-known effectual strategy to achieve sustainable development. To achieve economic growth with environmental sustainability has been considered a significant objective by governments worldwide. Inclusive green growth can be a practical approach to poverty alleviation. Depletion of natural resources or the deteriorating environment can be a risk to economic growth [5]. Government policies are significant in quickening the advertisement of green growth and accomplishing sustainable development goals [6].

China initiated the Belt and Road Initiative (BRI) to promote global energy security, enhance regional integration, and include green growth. The BRI energy cooperation is constructive in breaking down energy barriers, encouraging resource exchange, optimizing energy structure, and progressively increasing green factor productivity [7]. The BRI includes more than 71 countries, covering almost 65% of the worldwide population [8]. Most of the countries in this project are emerging economies, unlike developed economies, in terms of employment structure, usage of resources, and productivity. Developing countries have great environmental pollution [9]. In order to accomplish sustainable development goals and to alleviate the influence of global climate change, countries are paying more importance to green growth globally [9].

We have considered the BRI region as it has a large population, vast territory, and swift economic growth due to vigorous regional collaboration. All countries in the BRI region have different resource endowments, economic growth, and geographical location. This region has abundant natural resources, so it is considered the engine of the worldwide economy. This region’s inclusive green growth targets would benefit the worldwide economy [4].

Due to rapid urbanization, growing concerns about the natural environment and human well-being have encouraged policymakers to rethink the economic growth model. The role of good governance is mandatory in the new model to re-balance social, economic, and environmental goals. Good governance ensures complementarity amongst environmental protection, enhancing human development and economic growth [10]. Good governance’s influence on sustainable development/inclusive green growth has become a worldwide subject of focus [11].

Inclusive green growth (IGG) has been considered a dependent variable that determines what has been accomplished after economic growth; the essentiality of ‘greening’ the national income accounts for considering environmental degradation. The inspiring landscapes are the depreciation of natural capital comprised of the collective pointers of net national output. National savings and educational expenditures are the vital components that must be paid attention to in order to ensure the country’s steady economic working and inclusive green growth. The dependent variable is inclusive green growth based on the genuine savings index/adjusted net savings (ANS), including particulate emission damage proxy. This is the first study that has used the proxy of ANS for measuring IGG, contributing to the literature. This research is supposed to answer the following questions:

• How does urbanization affect IGG?
• What would be the impact of governance on IGG?
• How would the governance index moderate the association between urbanization and IGG?
• What would impact per capita income, unemployment rate, exchange rate, and household consumption per capita on urbanization and IGG?

Most previous researchers have measured the IGG at the firm level/limited data set. Alrasheedi et al. [12] evaluated IGG development at the firm level and suggested measuring inclusive green growth development at the country level, so current research includes 64 BRI countries in the data set. We selected BRI economies because few empirical studies have been focused on this group of economies. The BRI region includes countries from Asia, Europe, and Africa, so it is the best data set. BRI countries have a well-educated population, tourism scope, openness for trade, diversification, and economic growth, so it is a perfect data set for this research. The current research contributes to the literature by exploring the effect of urbanization on IGG in the presence of important control variables such as unemployment, per capita income, exchange rate, and household consumption that have been ignored in the previous studies. For the BRI region, the effect of urbanization on IGG was never explored, and the moderating effect of governance was not considered between urbanization and IGG. Thus, current research emphasizes how urbanization impacts inclusive green growth with the moderating impact of governance by using country-level aggregated data from 2004 to 2019. It also facilitated the verification of whether unemployment, exchange rate, per capita income, and household consumption would affect the inclusive green growth in the BRI region or not, which offers a vigorous contribution to the literature.

2. Hypotheses Development and Research Methodology

2.1. Inclusive Green Growth

Recently, the concept of inclusive green growth received remarkable attention from scholars. Alrasheedi et al. [12] introduced the new approach IVIF-CoCoSo to recognize and rank the indicators of IGG to sustainable manufacturing. The evaluation and ranking of IGG indicators were conducted on the basis of a literature review and surveys from manufacturing sector experts. Hao et al. [5] evaluated the impact of green growth on sustainable environment/CO₂ emissions from 1991 to 2017 for G7 countries by utilizing the CSAARDL model. Results indicated that green growth, human capital, renewable energy, and environmental taxes should reduce CO₂ emissions, while environmental depletion was observed due to GDP growth. Li et al. [4] recognized that the IGG merges social inclusion, environmental protection, resource conservation, and economic development in the Asia–Pacific region. The country’s economic growth can affect the IGG level. Pardo Martínez and Cotte Poveda [13] stated that technology, innovation, and science are mandatory to achieve green growth and sustainable development goals in Colombia. Sustainable development goals (SDGs) and green growth were two important strategies to enhance productivity, competitiveness, and environmental protection. This study showed that green growth is associated with SDGs as it brings economic growth without harming the natural resources and natural environment that is mandatory for social welfare. He et al. [9] stated that some countries of the BRI region have fast economic growth affecting the environment, so the rate of inclusive green growth is slow due to poor environment. Thus, developing countries must share emission-reduction technologies for environmental protection to promote green growth. The association of inclusive green growth with urbanization, governance, and other socioeconomic factors is explained below with literature support.

2.2. Urbanization and Inclusive Green Growth

According to the UN report of 2010, urbanization represents the urban population proportional to the country’s whole population [14].

Wilkerson et al. [15] centered on topics related to green spaces, such as parks, street trees, gardens, and natural features. This study focused on the fact that the socio-economic factors are very important for green spaces in cities as these factors can influence people’s demands regarding ecosystems and management decisions and are beneficial for human well-being. Chen et al. [16] evaluated the correlations between urbanization, human capital, and ecological footprints by utilizing data from 110 countries from 1990 to 2016 and observed the negative influence of accelerated urbanization/population size on environmental quality.

Liu [1] focused on the association between per capita CO₂ emissions, urbanization, renewable energy, non-renewable energy, and real output for China, Mongolia, Japan, South Korea, and Russia for 20 years (1995–2014). The results showed that urbanization has enhanced CO₂ emissions. Zhang et al. [17] suggested that the construction of residential buildings, population size, economic growth, and level of technology were the main cause of CO₂ emissions for three regions in China from 2008 to 2017. The urbanization ratio was negatively associated with CO₂ emissions, and an inverted U-shaped association was observed amongst the urban economic development CO₂ emissions.

Shang and Liu [3] focused on the interrelationship between urbanization and regional development. This study evaluated the impact of the eco-environment of the coastal cities of China due to human activities while achieving economic growth and suggested that inclusive green development (GD) is a socially inclusive and environmentally friendly method of economic growth. Established on the literature review, our first hypothesis is as follows:

H1. 

Urbanization has a significant negative influence on inclusive green growth.

2.3. Role of Governance in Urbanization and Inclusive Green Growth

Governance/institutional quality indicates the institutions’ values that administrate government property rights, constitution, laws, and traditions crucial for the personal association among the stakeholders [18]. Previous scholars have claimed that good governance can play a significant role in sustainability in natural-resource-abundant economies [19].

Governance has a significant impact on the economic development of any country by introducing best practices in governmental interventions and bringing sustainability to the country. A transparent institutional framework progresses public finances management and opposes social corruption. Carbonnier et al. [20] observed the dynamic association amongst institutional quality, armed violence, resource extraction, and sustainable development for 96 emerging economies over the period of 24 years. Armed conflict and armed violence were found to influence ANS per capita negatively. Especially in resource-rich countries, good governance is mandatory for development.

Further, Stoever [21] recognized good governance’s economically substantial and positive effect on sustainability. Moreover, Meyer et al. [22] highlighted that good governance is compulsory for sustainability. Bekhet and Latif [23] also observed the importance of the institutional governance quality of Malaysia on sustainable development from 1985 to 2015. Danish and Ulucak [24] investigated the association amongst governance, CO₂ emissions, renewable energy consumption, and economic development from 1992 to 2015 for 18 Asia–Pacific Economic Cooperation economies. Unidirectional causality was observed from governance/institutional quality to CO₂ emissions. Omri and Mabrouk [10] demonstrated the effectiveness of good governance in balancing environmental, social, and economic components of sustainable development for 20 MENA countries from 1996 to 2014. The results observed that institutional and political governance positively contributed to sustainability’s environmental, social, and economic components. Consequently, it is supposed that governance is a crucial element for urbanization and determining factor of inclusive green growth/sustainability.

Established on the literature review, our hypotheses are as follows:

H2. 

Governance positively influences inclusive green growth or vice versa.

H3. 

The interaction term of governance and urbanization positively influences inclusive green growth or vice versa.

2.4. Other Socio-Economic Factors Affecting Inclusive Green Growth

Unemployment, per capita income, household consumption per capita, and exchange rate also affect the IGG. Akeju and Olanipekun [25] stated that Nigeria’s unemployment rate has increased due to rapid population growth. This study tested Okun’s law, which proved the negative association amongst the unemployment rate and economic growth. A relationship was observed amongst the unemployment rate and output growth, and it was suggested that FDI (foreign direct investment) could decrease unemployment in Nigeria. Kaimuri and Kosimbei [26] examined the determinants of sustainability in Kenya from 1991 to 2014 using the autoregressive distributed lag (ARDL) model. Adjusted net savings rate (ANSR) sustainable development proxy was ANSR (adjusted net savings rate). The results observed that unemployment rate and energy efficiency both negatively impacted sustainability in the short run while household consumption per capita negatively influenced the sustainability in the long run. Sirah and Atilaw [27] evaluated that the unemployment rate is increasing in developed countries. The situation has been worst in developed economies because unemployment and economic growth rates are not balanced. In Ethiopia, the COVID-19 pandemic has enhanced this unbalance and increased the unemployment rate due to economic recession. Hjazeen et al. [28] inspected the impact of unemployment on Jordan’s economy and exhibited the negative association amongst the unemployment rate and economic growth. On the basis of the literature review, our hypothesis is as follows:

H4a. 

The unemployment rate has a negative impact on inclusive green growth.

Per capita income is a much-needed element in tracking the inclusive green growth of the country. Urbanization is a driver of economic growth, and unbalanced development amongst urban and rural areas is causing the migration of people to urban centers as it would raise their income along with other urban facilities. Per capita income is crucial for sustainability/inclusive green growth; thus, Pardi et al. [29] measured the influence of per capita income and inflation rate on adjusted net saving (ANS) and proved the existence of a correlation between these variables. Additionally, Koirala and Pradhan [30] determined the factors that determined sustainability for 12 Asian economies and evaluated the positive influence of per capita income on sustainability. Thus, the role of per capita income is very important for inclusive green growth. On the basis of the literature review, our hypothesis is as follows:

H4b. 

Per capita income positively impacts inclusive green growth.

Ariff and Zarei [31] discussed currency instability using 15 different currencies from developed and developing countries. Exchange rates should be managed to promote sustainable economic growth. Country risk factors and parity factors are interconnected with exchange rate relative volatility. Thus, exchange rate stabilization is beneficial to determine inclusive green growth. On the basis of the literature review, our hypothesis is as follows:

H4c. 

Exchange rate positively impacts inclusive green growth.

Household consumption comprises the market value of services, goods, and payments to governments to obtain licenses/permits by the households. Household consumption is a vital determining factor for inclusive green growth. Households’ consumption will reduce if there is uncertainty in an economy [32]. Liu et al. [33] explored how household consumption and technology development impacted the CO₂ emissions in China from 1992 to 2002. Technological development has reduced CO₂ emissions while household consumption increased CO₂ emissions and affected sustainable development. Kaimuri and Kosimbei [26] centered on the determinants of sustainability for Kenya from 1991 to 2014. ANSR measured sustainability. The autoregressive distributed lag (ARDL) model was used to determine the long-run association between household consumption per capita, resource productivity, real gross domestic product per capita, unemployment rate, terms of trade, and energy efficiency. Results showed that unemployment negatively impacted ANSR in the short run while household consumption per capita negatively influenced ANSR in the long run. Molnar et al. [34] focused on the association amongst urbanization and consumption behavior in China. Urbanization increased consumption by 30%, which is unbalancing the economy. Therefore, household consumption is an important determining factor for inclusive green growth. On the basis of the literature review, our hypothesis is as follows:

H4d. 

Household consumption has a negative impact on inclusive green growth.

2.5. Data and Measurement

This research measured the influence of urbanization on IGG from 2004 to 2019 for 64 BRI countries with the moderating role of governance. Control variables were unemployment, per capita income, household consumption per capita, and exchange rate. Appendix A contains the list of 64 sample countries. Appendix B contains the definitions and data sources of variables. It is better to research macro-economies and global and regional studies, and finance using panel data and time series. Stata 15 (College Station, TX, USA) has been used for the analysis as it is very efficient for panel data analysis. Panel data methods are most suitable for determining the factors affecting annual net savings (ANS) for a large sample of 64 countries over the period of 16 years. Panel data control heterogeneity issues in the data set. Urban population growth (annual %) was used as a proxy for urbanization data that the UN provided and was recommended by [32].

Adjusted net saving (ANS) is also known as the genuine saving index, and it consists of economic, social, and environmental factors. ANS includes dimensions of economic, social, and environmental and CO₂ emission damage. Thus, it greatly determines saving by including social capital, natural capital, knowledge stock, economic worth, and physical capital. IGG was measured by the adjusted net saving index (ANSR), and data were taken from the World Bank. ANSR has been used extensively by multiple previous studies such as by Ullah et al. [35] and Iftikhar et al. [32] to measure sustainability. However, this is the first study that has used ANS to measure inclusive green growth.

The moderator governance comprises six indicators: regulatory quality, the rule of law, control of corruption, political stability and absence of terrorism, government effectiveness, and voice and accountability. These governance indicators are vital factors for the country’s sustainability/inclusive green growth, so governance was used as a moderator for this study. Governance quality plays a significant role in trade and travel amongst countries. Multiple previous studies used these governance/institutional quality variables, such as those by Ullah et al. [32,35,36], Xu et al. [37], Sheraz et al. [38], and Ullah et al. [39], and data were are taken from the World Bank.

Due to the unemployment rate, urban poverty is increasing, affecting countries’ economic growth. Therefore, the unemployment rate is very important to consider when determining urbanization’s impact on inclusive green growth. Unemployment was measured by “unemployment rate as of total population”, and data were taken from the World Bank. Per capita income defines the development of the country. Currently, most BRI countries’ population is moving to urban areas to increase their per capita income. Per capita income was measured by “gross national income divided by midyear population”, data were taken from the World Bank, and it was recommended by [36]. Household consumption was measured by “households and NPISHs final consumption expenditure per capita growth (annual %)”, data were taken from the World Bank, and it was recommended by [32]. The exchange rate was measured by “local currency units per U.S. dollar”, data were taken from the IMF, and it was recommended by [32].

2.6. Model Framework

The framework of the research is demonstrated in Figure 1.

The Solow neoclassical growth theory focuses on economic growth, one of the most suitable theories for this research [40]. We used Solow’s neoclassical model to observe the impact of urbanization (UP) on “IGG” for the BRI region. Solow’s economic theory states that a steady economic growth rate combines three driving forces: capital, labor, and technology. Two economists, Trevor Sawn and Robert Solow, established the long-run economic growth model in 1956, considering that population growth increases the economic growth rate. Solow incorporated technology in this model in 1957, which deliberates that the impact of labor and capital is limited and that which contribution of technology in development is long-lasting. Robert Solow extended the Solow neoclassical growth model on the basis of the modern theory of economic growth in that savings lead towards growth and development is determined by technological progress only at the exogenous level.

Moderator is governance (GI). Control factors are unemployment rate (UER), household consumption (HC), per capita income (PCI), and an exchange rate (ER), and µ = error term. Thus, the model correlation can be stated as follows:

IGG = ∫ (UP, GI, PCI, HC, ER, UER)

(1)

An econometric two-step sys-GMM dynamic model is explained as follows:

IGG_i,t = β₀ + β₁ (IGG)_i,t−1 + β₂ (UP)_i,t + β₃ (PCI)_i,t + β₄ (HC)_i,t + β₅ (ER)_i,t + β₆ (UER)_i,t + µ

(2)

An econometric moderating effect model can be written as follows:

IGG_i,t = β₀ + β₁ (IGG)_i,t−1 + β₂ (UP)_i,t + β₃ (GI)_i,t + β₄ (UP*GI)_i,t + β₅ (PCI)_i,t + β₆ (HC)_i,t + β₇ (ER)_i,t + β₈ (UER)_i,t + µ

(3)

The two-step GMM technique is the most suitable for our dataset and inspects over-identifying restrictions, measurement errors, auto-correlation, and endogeneity issues in the panel dataset [41]. N (number of cross-sections) should be greater concerning the number of the period for using this technique. GMM (generalized methods of moments) techniques provide better outcomes with correct model specifications than single-equation techniques/models. The two-step GMM is the best technique when we have no idea of the distribution of the dependent variable. The lag-value of IGG has been considered in GMM to make it a dynamic model and to overcome the issue of autocorrelation as prevalent in the static regression model. Thus, GMM provides the most accurate and efficient estimates by controlling the lag-effect of its dependent variable, “IGG”. The values of inclusive green growth would rely on its previous values to predict the future values more accurately. The GMM technique has been recommended by many scholars for the panel dataset [32,35,36,37,38,42,43,44,45]. The Hansen test was conducted to observe the over-identifying restrictions. The AR (1) p-value must be less than 0.05, and the AR (2) p-value must be larger than 0.05.

3. Results and Discussion

3.1. Descriptive Summary Results

Table 1. Descriptive statistics.

Variables	Obs	Mean	Std.Dev.	Min	Max
Green growth	1024	10.752	11.768	−15.86	38.201
Urbanization	1024	2.051	1.979	−1.422	8.327
Governance index	1024	0.001	0.992	−1.757	3.405
Urbanization*Governance index	1024	−0.172	2.872	−8.292	21.788
Unemployment	1024	7.664	5.936	0.140	37.25
Per capita income	1024	3.560	3.977	−8.444	14.875
Exchange rate	1024	749.206	2637.302	0.280	16,302.25
Household spending per capita	1024	419.732	497.686	8.180	2326.37

displays the descriptive statistics, including observation count, mean, standard deviation, minimum and maximum values for the moderator, dependent variable, independent variable, and control variables. Table 1 exhibits 1024 observations for a sample of 64 countries from 2004 to 2019. Outcomes show that all values were within the range.

Table 2. Pairwise correlations.

Variables	1	2	3	4	5	6	7	8	VIF
IGG	1								-
Urbanization	0.112 ***	1							1.448
GI	0.248 ***	−0.088 ***	1						2.562
Urban*GI	0.338 ***	0.174 ***	0.712 ***	1					2.352
UER	−0.334 ***	−0.358 ***	0.037	−0.059 *	1				1.228
PCI	0.068 **	−0.185 ***	−0.127 ***	−0.145 ***	−0.073 **	1			1.172
ER	−0.073 **	0.152 ***	−0.189 ***	−0.204 ***	−0.210 ***	0.084 ***	1		1.13
HC	0.173 ***	−0.180 ***	0.515 ***	0.350 ***	0.005	−0.250 ***	−0.181 ***	1	1.527
Mean VIF									1.631

*** p < 0.01, ** p < 0.05, * p < 0.1.

demonstrates the VIF (variance inflation factor) for the study’s independent variable, moderator, and control variables to ensure that multi-collinearity does not exist in the data. If the value of VIF is greater than 5 for any variable, then it specifies that there is a multi-collinearity issue [46]. As suggested by—and all VIF values should be lower than the threshold of 5 to confirm that results are not affected by multicollinearity issues [35,36,39,42].

As VIF values were less than 5, our dataset had no multi-collinearity. Table 2 also exhibits the pairwise correlation amid all the variables. The outcomes of Table 2 signify that urbanization and governance had a significant and statistically positive relationship, at 11.2% and 24.8%, respectively, with inclusive green growth at a 1% significance level. Per capita income and household consumption were also positively associated with inclusive green growth, while unemployment and exchange rates were negatively associated with inclusive green growth.

Table 3. Second-generation unit root Pesaran CIPS test.

Variables	Level	First Difference	Decision Order
Green growth	−2.663 **	-	I(0)
Urbanization	−2.495	−2.768 ***	I(1)
Governance index	−2.557 **	-	I(0)
Unemployment	−2.221	−3.380 ***	I(1)
Per capita income	−3.487 ***	-	I(0)
Exchange rate	−2.223	−2.761 ***	I(1)
Household spending per capita	−2.278	−3.448 ***	I(1)
Green growth	−2.663 **	-	I(0)

***, and ** indicate significance level at 1%, and 5%, respectively.

exhibits the second-generation unit root Pesaran CIPS test, which indicated that all the variables were significant at the level or first difference. Hence, the dynamic nature of the two-step sys-GMM model is fit to use (Appendix C).

Table 4. Results of panel co-integration test.

Variables	Level	First Difference	Decision Order
Westerlund test
Variance ratio	Yes	8.0598 ***	Ha: panels were cointegrated
Pedroni test for cointegration	Kernel: Bartlett with (2.00) Newey–West lags and Augmented lags: 1 (AIC) Pedroni test for cointegration
Modified variance ratio	Yes	−7.1700 ***	Ha: panels were cointegrated
Modified Phillips–Perron t	Yes	6.9300 ***
Phillips–Perron t	Yes	−11.1408 ***
Augmented Dickey–Fuller t	Yes	−11.8507 ***

Note: countries, 64, and time, 16 years; *** p < 0.01 indicate significance at 1% levels.

exhibits the results of the panel co-integration test, which shows the variance ratio of the Westerlund test was significantly positive at 9.0598 in favor of the alternate hypothesis acceptance that all panels are co-integrated well. Moreover, Pedroni test values also indicated that the panels were well co-integrated.

3.2. Results of Two-Step System GMM Method

Table 5 measures the inclusive green growth estimation with a two-step system GMM and robust with D-K regression. Column (1) exhibits the two-step system GMM final model by indicating that the coefficient of direct IGG lags value was positive (0.254) with a p-value of less than 5%, which signifies the dynamic nature of green growth. The urbanization coefficient was negative (−8.270), with a p-value of less than 10%, demonstrating that a percent increase in the urban population will hurt the green growth and level of savings. Moreover, in column (2), the moderating role of the governance index also showed a negative but significant impact on green growth at the significance level of 10%. Conversely, the interaction term of urbanization and governance showed a significantly positive impact on green growth at the significance level of 5%. In column (1), per capita income had a positive and significant impact on IGG at a 10% significance level. However, unemployment, household consumption, and exchange rate negatively influenced IGG.

On the other hand, as shown in column (2), when we considered the modernization effect, it showed that per capita income positively and significantly influenced the IGG with a 0.198 value at a 5% significance level, while household consumption was also positive but not significant with a 0.003 value. Moreover, the unemployment rate negatively impacted the IGG with a −0.882 value at a 1% significance level, and the exchange rate with a −0.025 value also hurt the IGG. It indicates that urbanization negatively contributed to IGG in the BRI region from 2004 to 2019. The p-value of Arellano–Bond (AR1) was less than 5%, whereas the p-value of second-order difference’s AR2 was more significant than 5%. Furthermore, the numbers of instruments were 22, which was smaller than our 64 groups, so the validity of the two-step system GMM instruments was thus confirmed. In addition, the Wald test chi-square verified that the model is fit to use because the p-value was smaller than 1%. The diagnostic test outcomes specify that estimation techniques are reliable and correct as all assumptions are accurate.

The Driscoll–Kraay standard errors methodology was used for the robustness check. The D-K regression identifies the robust estimator’s cross-sectional dependence (CD). This technique incorporates the weighted autocorrelation and heteroscedasticity compatible estimator (HAC) values and a stranded error into the weighted HAC values amid the variables and residuals. The Driscoll–Kraay standard errors method has been deliberated as one of the most impactful procedures for data heteroscedasticity and spatial and serial dependence. It is an impeccable method for panel equilibrium and unbalanced values as it combines all types of temporal dependency and cross-sectional dependence (CD) [35,42,47].

Column (3) shows that urbanization had a negative (−0.923) coefficient with a 5% significance level, demonstrating that a percent increase in urban population will hurt green growth. Moreover, in column (4), the moderating role of the governance index also showed a significant and negative impact on green growth with a −0.590 value at the significance level of 5%. Conversely, the interaction term of urbanization and governance showed a positive and significant impact on green growth at the significance level of 1%. Column (4) shows that per capita income positively impacted IGG with a 0.267 value at a 1% significance level, similar to the main GMM model. In contrast, the unemployment rate negatively influenced IGG with −0.500 at a 5% significance level, also similar to the main GMM model. Moreover, the exchange rate was positive but not significant in both columns (3) and (4), while household consumption was negative and significant in column (3) but positive in column (4) but not significant. Thus, the findings of D-K regression confirmed the two-step sys-GMM and validated the fact that urbanization negatively contributes to IGG in the BRI region from 2004 to 2019.

Table 5. Results of urbanization effect on inclusive green growth when considering the moderating role of governance index.

	1	2	3	4
Dependent variable: green growth	Main Sys-GMM model (1–2)		D-K S. E. fixed effect model (3–4)
L. green growth	0.254 **	0.318 **
	(0.666)	(0.048)
Urbanization	−8.270 *	−2.140 ***	−0.923 **	−0.800 *
	(6.291)	(0.640)	(0.369)	(0.468)
Governance index		−7.139 *		−0.590 **
		(3.902)		(0.553)
Urbanization*governance		2.843 **		0.216 ***
		(1.178)		(0.207)
Unemployment	−2.060	−0.882 ***	−0.502	−0.500 **
	(1.561)	(0.173)	(0.096)	(0.099)
Per capita income	1.640 *	0.198 **	0.270 *	0.267 ***
	(1.456)	(0.095)	(0.072)	(0.073)
Exchange rate	−0.011	−0.025	0.002	0.008
	(0.001)	(0.003)	(0.001)	(0.005)
Household consumption per capita	−0.002 *	0.003	−0.001 *	0.006
	(0.005)	(0.002)	(0.002)	(0.003)
Year effect	Yes	Yes	-	-
Observations	960	960	1024	1024
Post Analysis
AR1	−0.692	−1.618
AR1 (p-value)	0.0003	0.0012
AR2	0.431	0.246
AR2 (p-value)	0.667	0.806
Sargan test	0.143	562.2
Sargan (p-value)	0.705	0.813
Hansen	0.492	36.09
Hansen (p-value)	0.261	0.150
J. statistics (Number of Instruments)	22	43
Wald test (CHI2)	87.13	725.1
Wald test (CHI2) p-value	0.0000	0.0000
R-square			0.6475	0.6951
Maximum lag			2	2
F statistics			0.0003	0.0000
Number of countries	64	64	64	64

Note: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1 indicate significance at 1%, 5%, and 10% levels, respectively. We used Stata xtabond2 command for sys-GMM [41] and xtscc command [48].

Table 5. Results of urbanization effect on inclusive green growth when considering the moderating role of governance index.

	1	2	3	4
Dependent variable: green growth	Main Sys-GMM model (1–2)		D-K S. E. fixed effect model (3–4)
L. green growth	0.254 **	0.318 **
	(0.666)	(0.048)
Urbanization	−8.270 *	−2.140 ***	−0.923 **	−0.800 *
	(6.291)	(0.640)	(0.369)	(0.468)
Governance index		−7.139 *		−0.590 **
		(3.902)		(0.553)
Urbanization*governance		2.843 **		0.216 ***
		(1.178)		(0.207)
Unemployment	−2.060	−0.882 ***	−0.502	−0.500 **
	(1.561)	(0.173)	(0.096)	(0.099)
Per capita income	1.640 *	0.198 **	0.270 *	0.267 ***
	(1.456)	(0.095)	(0.072)	(0.073)
Exchange rate	−0.011	−0.025	0.002	0.008
	(0.001)	(0.003)	(0.001)	(0.005)
Household consumption per capita	−0.002 *	0.003	−0.001 *	0.006
	(0.005)	(0.002)	(0.002)	(0.003)
Year effect	Yes	Yes	-	-
Observations	960	960	1024	1024
Post Analysis
AR1	−0.692	−1.618
AR1 (p-value)	0.0003	0.0012
AR2	0.431	0.246
AR2 (p-value)	0.667	0.806
Sargan test	0.143	562.2
Sargan (p-value)	0.705	0.813
Hansen	0.492	36.09
Hansen (p-value)	0.261	0.150
J. statistics (Number of Instruments)	22	43
Wald test (CHI2)	87.13	725.1
Wald test (CHI2) p-value	0.0000	0.0000
R-square			0.6475	0.6951
Maximum lag			2	2
F statistics			0.0003	0.0000
Number of countries	64	64	64	64

Note: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, and * p < 0.1 indicate significance at 1%, 5%, and 10% levels, respectively. We used Stata xtabond2 command for sys-GMM [41] and xtscc command [48].

4. Discussion

This research employed a two-step system GMM technique to measure the impact of urbanization on inclusive green growth. Further, overall findings were validated by DK standard-error regression, and all outcomes were validated. First, the outcomes of descriptive statistics showed that all values were within the range. Then, the VIF test results indicated no multi-collinearity issue in the data as all the values being less than 5.

The outcomes of the two-step system GMM validated the dynamic nature of inclusive green growth, indicating that all the countries of BRI are on the path of inclusive green growth. Urbanization brings about multiple benefits for human beings, e.g., employment opportunities, high transportation facilities, educational opportunities, modernized equipment, and higher wages. Still, it has exploited natural resources, which has deteriorated the environment. It is hurting biodiversity and producing land, water, and environmental pollution. It is also increasing traffic issues, accidents, and shortage of workers for agriculture. The outcomes of the results validated the fact that urbanization negatively affected inclusive green growth. It means that if the rate of urbanization is increasing, it will decrease the inclusive green growth of the country and the BRI region. The study’s first hypothesis (H1) specifies that urbanization would negatively influence IGG.

Our results confirmed the hypothesis (H1) in line with [1]. This study’s second hypothesis (H2) is that governance positively influences inclusive green growth or vice versa. The role of good governance is very important for environmental protection, enhancing human development and economic growth, but the role of governance was found to be negative for 64 BRI countries. The hypothesis (H2) results are consistent with a previous study [20]. Poor governance and political risk hurt the economic growth of the country. Our third hypothesis (H3), the moderating interaction term of urbanization and governance (urban*gov), was positive at 5%. It means that good governance and urbanization planning would increase inclusive green growth in the BRI region. The outcomes confirmed that governance plays a decisive role in sustainable development and inclusive green growth. Therefore, countries must focus on improving the urbanization population and governance management practices, especially in BRI countries [39].

The unemployment rate hurts the economic growth of the country. Our results support hypothesis H4a and the study of [26]. It indicates that the unemployment rate will hurt the IGG [32]. Per capita income positively impacts IGG, and our results support our hypothesis H4b and align with [30]. This shows that per capita income has positively contributed to the IGG.

The exchange rate is a very important control factor, and exchange rate stabilization plays an important role in the country’s growth. However, the role of the exchange rate was not found to be significant for this study, so H4c was not supported.

Household consumption is a vital element in determining the growth of the country. In this study, household consumption negatively impacted IGG, which supports our hypothesis H4d, and our results are consistent with the work of [26].

5. Conclusions

This research addressed the influence of urbanization on IGG in 64 countries of the BRI region from 2004 to 2019. The dependent variable inclusive green growth was determined by ANS (adjusted net savings). Independent variable urbanization data was taken from the United Nations database. This study aimed to measure the effect of other variables such as unemployment rate, exchange rate, household consumption, and per capita income as control factors on inclusive green growth. Very important control variables were used in this study, which were almost ignored in previous studies. This research was conducted for the BRI region, comprising developed and emerging economies of Asia, Africa, and Europe. Previous studies focused on inclusive green growth at the firm or country level, but no previous research has focused on this group of economies. A two-step system GMM technique was used for this research. Moreover, the Driscoll–Kraay standard errors methodology was used for the robustness check.

GMM confirmed the positive dynamic nature of inclusive green growth in the BRI region. This research theoretically contributes the idea that the direct impact of urbanization and moderating impact of governance has contributed negatively to inclusive green growth. Per capita income promotes inclusive green growth, while household consumption and the unemployment rate have negatively impacted inclusive green growth. The exchange rate has no significant impact on inclusive green growth. The interaction among urbanization*governance was found to be positive, suggesting that the role of good governance is mandatory for sustainable urbanization. The Driscoll–Kraay standard errors robustness check model also validated the two-step system GMM findings.

Regarding inclusive green growth, the countries along with the BRI region should focus on eco-innovation and strict environmental regulations as inclusive green growth can be an essential strategy for accomplishing sustainable development. Environmental taxes should be imposed to decrease CO₂ emissions so investors can focus on environmentally friendly projects. Non-renewable energy resources should be replaced with renewable energy sources to decrease CO₂ emissions. Environmental sustainability and poverty alleviation targets can be achieved via inclusive green growth [5].

According to [49], agriculture development can improve BRI regional cooperation as it is a fundamental means of sustainable development. Previously, most BRI countries’ populations lived in rural areas and depended on agriculture for their income. The urbanization rate is increasing, and more and more people are moving from rural to urban areas for a better lifestyle. However, it is increasing living costs, traffic issues, accidents, poor nutrition, health issues, environmental issues, and a shortage of workers for agriculture. Thus, instead of focusing on urbanization growth, BRI must rethink introducing new technologies for agriculture development, as well as funding for small-scale industries in rural areas, and budget should be equally distributed for rural and urban areas.

The unemployment rate is affecting the economy’s overall growth as poverty and crime rates are increasing due to unemployment issues, which is harmful to the inclusive green growth of the region. There are more than 70 countries in the BRI project. Some countries have rapid economic growth, a few are working on great projects regarding poverty alleviation, and some have good environmental performance. All the countries in the BRI region must develop regional cooperation policies regarding per capita income, poverty reduction, and inclusive green growth to achieve goals of common interest. The exchange rate fluctuations affect the trade and travel plans, and therefore stabilizing the exchange rate is very important for BRI countries. The impact of governance was negative on inclusive green growth, suggesting that the government needs to work on governance quality as it affects the country’s growth. Political risks must be reduced to safety, security, and improving diplomatic connections as it will boost trade among countries.

Regarding governance, BRI countries must concentrate on improving institutions’ structure. BRI countries, regarding a strong institutional framework, should sign some agreements. A strong institutional framework can help governments to bring inclusive green growth to the region. Agreements must focus on effective environmental regulations without compromising social and economic growth. Good governance can benefit equal income distribution, improving environmental quality [24].

In addition to the contributions and implications, our research work has a few limitations. There are more than 70 countries in the BRI region, but only 64 BRI countries were considered for this research as data for these variables were unavailable for some BRI economies. The research period was also from 2004 to 2019 due to the unavailability of data, but the future researcher can conduct research for more than 70 countries with an updated time period.

Secondly, the governance index was used as a moderator in this research, and we considered typical formal governance indicators (six indicators from the WGI database). Future researchers can consider 12 governance indicators from the International Country Risk Guide (ICRG) database for the robustness check.

Third, this study is based on Solow’s neoclassical growth theory, which is a modest framework for determining the long-term growth rate of countries. It is a widely accepted model for both theoretical and empirical studies. Solow’s economic theory states that a steady economic growth rate combines three driving forces: capital, labor, and technology. The technology factor was incorporated into this model in 1957. Robert Solow extended the Solow neoclassical growth model on the basis of the modern theory of economic growth in that savings lead towards growth and development is determined by technological progress only at the exogenous level. Inclusive green growth was measured by adjusted net saving (ANS)/genuine saving. ANS includes education expenses of the public sector while it does not contain the expenses of the private sector and R&D (research and development) expenditures. The addition of this expenditure could be an excellent addition to this index.

Fourth, this research considered important control variables influencing urbanization and inclusive green growth, but future researchers may consider other control variables such as inflation rate. Most of the BRI countries are low- and middle-income countries, and inflation rates could impact the urbanization and inclusive green growth of the BRI region.