Does the quality of institutions and education strengthen the quality of the environment? Evidence from a global perspective

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Does the quality of institutions and education strengthen the quality of the environment? Evidence from a global perspective

Chor Foon Tang

^a^,^*

, Salah Abosedra

^b

, Navaz Naghavi

^c

aCentre for Policy Research and International Studies, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia

bDepartment of Accounting, Finance and Economics, American University in the Emirates-Dubai, United Arab Emirates

cFaculty of Business and Law, Taylor’s University, Malaysia

a r t i c l e i n f o

Article history:

Received 25 August 2020 Received in revised form 31 October 2020

Accepted 9 November 2020 Available online 13 November 2020

Keywords:

Environmental degradation GDP

Human capital Institutions Renewable energy

a b s t r a c t

This paper explores the determinants of global environmental degradation by utilising a newly formu- lated conceptual framework to examine whether the quality of institutions in a country plays a moderating role on environmental degradation. This issue has become a widespread concern in academia but few studies have accounted for these moderating roles. This is the scientific novelty of this study in comparison to previously published works. The study utilises unbalanced panel data from 114 countries. The dynamic panel GMM estimator is deployed to estimate a newly constructed global environmental degradation model. Generally, ourfindings support the Environmental Kuznets Curve (EKC) hypothesis where we find the EKC has an inverted-U shape. More importantly, wefind that institutional quality and human capital facilitate the impacts of both foreign direct investment (FDI) and renewable energy in reducing environmental degradation. Thesefindings not only advanced the prior environmental literature but it also provides a clearer picture for policymakers on the channel of institutional quality and human capital in protecting the environment. Therefore, corresponding policy measures should focus on improving the quality of institutions and human capital to effectively reduce environmental degradation for sustainable development.

1. Introduction

Although economic growth remains the primary objective of many polities, pursuing a responsible growth policy that minimises environmental impacts is rapidly becoming the focus of many governments given the severe environmental degradation wrought by often breakneck and ill-conceived development initiatives. In fact, such initiatives have often been the cause of environmental spoliation as growth-oriented sectors such as manufacturing, resource extraction and tourism often generate damaging externalities such as greenhouse gas emissions, air and water pollution, deforestation and other forms of environmental damage [1e3].

Environmental degradation is often cited as a classic example of exploitative capitalism as little effort is expended by those directly responsible to alleviate the environmental externalities of

development due to costs and limited utility value. Consequently, this neglect has spawned a multitude of environment-related problems ranging from climate change to desertiﬁcation and pollution. The endorsement of the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 at the Earth Summit in Rio de Janeiro and the accession to the Paris Accords of 189 nations excepting Iran and Turkey are indicative of the global concern over the deteriorating state of Earth’s natural ecosystems.

The concerns of governments have also galvanised the academic community to assume a more direct role in elucidating the factors impacting environmental degradation to furnish policymakers with informed inputs with which to craft and implement more environmentally friendly policies. Much of these research efforts have been underpinned by the Environmental Kuznets Curve (EKC), a well-known theoretical framework that seeks to delineate the nexus between environmental degradation and output.

Essentially, the EKC hypothesises that the quality of the environment tends to degrade when output increases during the early stages of economic growth and that the magnitude of environmental degradation decreases after this output surpasses a certain threshold. Given the foregoing, the EKC predicts that the

*Corresponding author. Centre for Policy Research and International Studies, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.

E-mail addresses: [email protected],[email protected] (C.F. Tang),salaheddin.

[email protected](S. Abosedra),[email protected](N. Naghavi).

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relationship between environmental degradation and output is more likely to be in the form of an inverted U-shape rather than monotonic in orientation [4,5]. Such a trajectory is attributable to the fact that at the initial stages of development, countries tend to prioritise economic development over environmental concerns before reversing course as they attain a higher stage of development. As such, environmental destruction is postulated to be more serious in developing than in developed economies.

Numerous studies (see Refs. [6e9], using CO2emissions as their proxy for environmental degradation, have afﬁrmed the EKC hypothesis using both time series and panel data techniques. Never- theless, other studies (e.g. Refs. [10e18] have refuted the EKC hypothesis as they have established the existence of monotonic, U- shape, or N-shape relationships rather than the postulated inverted U-shape linkage between environmental degradation and output.

These contradictory outcomes raise the possibility that inappro- priate policy responses that are deleterious to the environment have been inadvertently implemented thus compromising both environmental quality and GDP growth. In addition, despite the existence of studies examining the role and impact of FDI and renewable energy in environmental degradation, these studies tended to underestimate the moderating effects of institutional quality and human capital (education) on FDI and renewable energy. Suchﬁndings on these indirect factors have signiﬁcant policy implications as they provide valuable insights to policymakers on the role of institutions and education in environmental degradation. Hence, policy formulation and implementation can be better calibrated to yield maximal impact.

Apart from seeking to validate the EKC hypothesis, this study also contributes to the body of knowledge by examining the role of FDI, human capital, renewable energy and institutions as well as their moderating impacts in mitigating environmental degradation.

In contrast to previous studies on the EKC hypothesis, we intend to provide evidence on both the direct and indirect effects of FDI, human capital, renewable energy and institutions on environmental degradation. Hence, apart from providing inputs for effective policy formulation, this study will also elucidate the reasons behind variances in environmental quality between countries. To achieve the aforementioned objectives, we extend the conventional EKC framework by incorporating FDI, education, renewable energy, and institutional quality as additional explanatory variables. To obtain reliable estimation results, the present study uses datasets from 114 countries spanning the 1986 to 2015 period. Finally, to address endogeneity issues, we utilise the dynamic panel Gener- alised Method of Moments (GMM) estimator proposed by Arellano and Bond [19] and Blundell and Bond [20] to estimate the relationship between environmental degradation and its determinants.

The remainder of the paper is organised as follows. Section2 provides a review of the relevant literature. Section3outlines the methodologies used in the present analysis. Section4analyses the data and discusses the keyﬁndings. Finally, Section5concludes the paper by offering insights and policy recommendations.

2. Review of past studies

The nexus between economic growth and CO₂ emission has been extensively analysed using various econometric techniques.

While some studies support an inverted U-shaped relationship between economic growth and CO2 emission [6,7,9,21,22], other studies note a rising curve [12,23] or an N-shaped curve [24]. In addition, other studies such as Franzen [25] and Diekmann and Franzen [26] observed that the attitude and behaviour of individuals towards the environment differ signiﬁcantly between those in high-income countries and those in low-income countries.

This is because the relative prosperity enjoyed by citizens of high-

income countries allows them to demand the quality environment as noted by Inglehart [27] who further observed that high incomes invariably fostered environmentally friendly attitudes amongst the citizenry. A great body of research opines that high income is the result of more resource allocation for environmentally friendly modes of production in high growth countries that eventually reduce CO2 emission [28,29]. This assertion is substantiated by research which links a higher level of GDP to a higher level of technology utilisation in production, which decreased CO2 emission. In this vein, the recent research put forward the policy pres- sure for developing renewable energy in high-income countries [30]. Conversely, Dunlap and Mertig [31] challenge the contention that citizens in low-income countries are less likely to have much concern for the environment as they argue that the quality of the environment does not depend on output but the moral vision of individuals. Tran et al. [32] found that human development help to improve environmental quality. This view further posits that income is a mechanism capable of changing people’s preferences as higher incomes frame expectations that governments will be more proactive in implementing environmentally friendly policies [27,33]. As such, per capita GDP plays a crucial role in the adoption of measures aimed at preventing environmental degradation.

These foregoing arguments rationalise the inclusion of the square of per capita GDP in our model.

Increased inflow of FDI in many regions in the world is often cited to be a double-edged sword. On the one hand, it provides capital financing, positive externalities and enhances economic growth [34]. On the other hand, it could lead to environmental degradation [35e37]. Although the relationship between FDI inflow and CO2 emission has not been accorded due emphasis, many studies [38e42] have documented a positive nexus running from FDI to environmental pollution in recipient countries. In this regard, the pollution haven hypothesis as proposed by Chichilnisky [43] and Copeland and Taylor [44] has gained credence as it posits the existence of a positive relationship between FDI inflow and CO2

emission. Essentially, the hypothesis postulates that pollution- intensive manufacturing ﬁrms intent on avoiding the cost of stringent environmental regulations will invariably seek to operate in countries where environmental regulations are lax [45]. In this regard, it is also argued that countries will accrue more FDI inﬂows if they deliberately ignore the imposition of environmentally friendly initiatives [46]. In fact, there is evidence suggesting that loose environmental regulations imposed by host countries designed to attract and retain foreign investors have often delete- riously impacted the environment of these countries and that competition for FDI has resulted in lax environmental regulations and low pollution taxes. To prove this contention, Omri, Nguyen and Rault [36] conducted a study on the link between FDI-CO2

emissions using a simultaneous equation modelling approach. They noted that FDI had a positive impact on CO₂emission thus affirming the pollution haven hypothesis. This justifies the diversion of recent literature towards the quality aspect of FDI [47] rather than the amount of FDI inflows which can ensure sustainable economic growth. However, other studies (e.g. Refs. [48e51] show that the influx of FDI may improve environmental quality by transferring

“clean or green”technology from investors to host countries. In fact, this pollution halo hypothesis [34] contends that there are no deleterious side effects from FDI inﬂow as investors are likely to transfer low-carbon technology, equipment and processes to the host country that will ultimately contribute to better environmental quality besides generating economic development. Simi- larly, discussing the spillover effects of FDI, Guang et al. [52]

showed that foreign enterprises capital focussed on high-tech in- dustry and services can contribute to reduce the energy intensity and have a positive impact on the environmental quality. In

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summary, the effect of FDI on environmental degradation is mixed as its effects depend on the quality and types of technology utilised as well as the proclivity amongst investors for sharing technology.

The quality of human capital is also recognised as an indis- pensable ingredient for development as education improves labour productivity, assists in the application of skills and knowledge besides inspiring and enhancing the innovative capacity of a country.

Such positive beneﬁts imply that countries should strive to provide quality and adequate education to their citizenry to foster economic growth and development. Previous studies indicate that improvements in education levels propagate two opposing effects on the environment. On the one hand, improvement in educational stan- dards would likely increase the consumption of non-renewable resources as well as access to polluting technologies, resulting in a negative impact on environmental quality. This is a consequence of higher educational attainment facilitating income growth which could subsequently facilitate access to polluting technologies (cars, planes, etc.) resulting in degradation of environment quality [53,54]. Additionally, the role of education in conditioning environmental behaviour has also been debunked by several studies (e.g. Refs. [55e59] which note that educational levels are negatively associated with pro-environmental behaviour.

Conversely, better education levels tend to direct individuals away from environmentally-damaging behaviour towards more efﬁcient use of energy resources, increased awareness about the importance of environment protection and encourage participation in activities that promote and support a clean environment.

Furthermore, it may also motivate them to be involved in political platforms that emphasise environmental protection. Some other research put forward a new angle in which educated workers are considered as a substitute for energy. From the point of view of these researchers, human capital promotes the technological innovation which in turn leads to more environmentally friendly production [3,60,61].

Balaguer and Cantavella [62]; in extending the EKC framework to incorporate the education variable to reduce observed bias on income coefﬁcients in earlier studies, modelled the two aforementioned perspectives regarding education’s impact on environmental quality utilising higher education data from Australia from 1950 to 2014. They noted that improvements in education levels progressively compensated for increases in per capita CO2emis- sions resulting from economic growth.

Meyer [63] employed a regression discontinuity strategy to estimate the rise in educational attainment due to changes in oblig- atory education laws in 20th century Europe, and its impact on environmental behaviour. The study conducted two Special Euro- barometer surveys in 2007 and 2011 on a representative EU sample.

These surveys inquired about pro-environmental behaviours amongst respondents, in addition to collecting demographic in- formation related to age, country of residence, and their education.

He notes that an extra year of education increases the probability that an individual would perform seven of the eight examined pro- environmental behaviours. These include using environmentally friendly travel modes, reducing disposables, separating waste for recycling, reducing energy consumption, purchasing environmentally labelled products, purchasing local items, and reducing car usage. Meyer’s findings on the relationship between education levels and pro-environmental behaviour are echoed elsewhere. For example, individuals with higher education tend to recycle more compared to those with less education [64], to purchase organics [65], save energy [66], increase consumption of renewable energies [67] and to sacrificefinancial well-being to improve environmental quality [68].

Institutional quality also plays an important role in determining environmental quality. Institutions refer to the overarching

superstructure framework in which economic activities are conducted [69]. The main constituents of the institutional framework are its legal, economic, political, and social components. Legal institutions denote the legal system, including legislation, law administration and enforcement while economic institutions are concerned with rules governing the market, theﬁnancial sector, the production and distribution process, labour, commercial trans- actions etc. Political institutions deal with governance and the country’s political system. Finally, social institutions relate to so- cietal values pertaining to education, health and social security.

Institutional quality has a strong positive impact on economic activities [70]. However, the impact of institutional quality on environmental management and protection is mixed. Some studies (e.g.

Refs. [71e73] using CO₂emission as proxy indicator demonstrate that lax institutional frameworks result in more pollution as countries harbouring such frameworks tend to prioritise economic growth over environmental protection. This is especially true amongst emerging economies where the per capita income is relatively low and institutional frameworks are mainly devised to favour economic development over environmental conservation.

However, another study linking the positive association between institutional quality and CO2 emissions attributes the reason for production inefﬁciencies emanating from the use of outmoded pollution control technologies [74]. Likewise, Wang et al. [75] also found a positive effect of control of corruption on CO2emissions in Brazil, Russia, India, China and South Africa (BRICS) countries across 1996 to 2015 period using the partial least square (PLS) regression model. However, the study claimed that the effects of economic growth, trade openness and urbanisation on CO2emissions can be minimised by controlling corruption.

Conversely, quality institutional frameworks are largely responsible for effective environmental protection by way of pollution control [76,77]. This is because better institutional quality can lead to higher income levels that in turn creates more awareness about the importance of pristine environments and better pollution control. Another reason for the negative relationship between institutional quality and CO₂emission is competition in emerging markets, which leads to higher efﬁciency and less emission [78]. Furthermore, studies (e.g. Refs. [79e81] have also demonstrated that better institutional quality promotes innovation and development of environmentally friendly techniques which help to decrease CO2 emissions. Ali et al. [82] investigated the impact of institutional quality on environmental quality in developing countries. The study empirically examined the dynamic impact of institutional quality on CO2emissions across 47 developing countries, using the dynamic panel GMM estimation tech- nique. Theﬁndings reveal that institutional quality reduces carbon dioxide emissions and hence decreases the level of environmental degradation in the countries investigated. Lau, Choong and Ng [83]

used panel data from 2002 to 2014 from 100 countries to study the role of institutional quality on the EKC using the GMM estimators.

They conﬁrmed the existence of the well-known inverted U-shaped relationship between economic growth and CO₂ emissions. Their results show that lowering corruption results in reduced CO2

emissions in high-income countries and that the rule of law has a negative effect on the CO₂ emissions in the high- and middle- income countries. Abid [84]; Ibrahim and Law [85]; LaBelle [86];

Komarek, Lupi and Kaplowitz [87]; Welsch [88]; Fredriksson and Svensson [89]; Damania, Fredriksson and List [90]; Midlarsky [91];

Matsuo [92]; Rentz [93]; and Rose [94] also attested that institutional quality is positively correlated with environmental quality.

Several signiﬁcant conclusions can be drawn from this review of previous studies. First, a vast majority of studies on the EKC hypothesis mainly focused on validating its existence. Second, the ﬁndings of past studies on the impact of institutions, FDI, and

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education on environmental degradation are contradictory. Third, the moderating impact of human capital and institutions on the role of FDI and renewable energy in environmental degradation has not been accorded due attention which otherwise would have yielded signiﬁcant inputs for effective policy formulation. There- fore, a noteworthy contribution of this study is investigating the prerequisite for the effective absorption of FDI and consumption of renewable energy which lessen the side-effect of environmental degradation. In this vein, the institutional quality and human capital have been utilised as the facilitating (moderating) factors on the relationship between FDI, renewable energy consumption and environmental degradation. In other words, the present study contributes to the body of literature by re-investigating the EKC hypothesis, while including the quality of institutions and educational level as a necessary condition in mitigating environmental degradation.

3. Model, data and econometric methods

3.1. Dynamic panel regression model of environmental degradation

According to the EKC hypothesis, environmental quality is mainly impacted by the level of output, but its effects are likely to be non-linear due to scale, composition and technical reasons [21,95]. Generally, most studies (e.g. Refs. [35,83,85,96] have found that the relationship between output and environmental degradation tends to be in the form of an inverted-U. Given that the aim of this study is to investigate the effect of foreign direct investment, renewable energy, human capital and institutional quality on environmental degradation, our empirical model can be speciﬁed as follows:

ED_it¼

b

0þ

b

1ED_it1þ

b

2GDP_itþ

b

3GDP_it²þ

b

4FDI_itþ

b

5RE_it þ

b

6HC_itþ

b

7INS_itþ

l

iþε_it

(1)

where ED_it is the environmental degradation measured by per capita CO2emissions, GDP_it is the per capita real gross domestic product (GDP), GDP²_it is the square of per capita real GDP, FDI_it denotes foreign direct investment, RE_itrepresents the consumption of renewable energy, HC_itis the human capital, while INS_itis the institutional quality. Finally,lirefers to country-speciﬁc effect and εit, the disturbance term.

In addition to the effects of FDI and renewable energy on environmental degradation, our study suggests that such degradation would also vary across countries due to differences in the quality of institutions and the level of human capital. Good institutions, such as better governance, stringent regulations, and control of corruption, are likely to improve environmental quality by eliminating FDI in polluting industries and encouraging the development of renewable energy sources as well as the utilisation of green technology. In addition, the level of human capital has a significant influence on FDI inflows and renewable energy. This is because people who are more educated are likely to behave in an environmental-friendly manner and would thus be inclined to oppose the influx of FDI into environmentally damaging industries and to criticise the utilisation of obsolete and polluting production technologies and to support the adoption of renewable and clean energy processes. Increased educational attainment levels also tend to improve innovative capabilities that would drive innovation and the development of clean energy production systems, which would ultimately improve environmental quality. Therefore, to examine the presence of these contingency effects, we augment Equation(1) by incorporating the interaction terms as additional explanatory

variables as presented in Equations(2)e(5).

ED_it¼

b

0þ

b

1ED_it1þ

b

2GDP_itþ

b

3GDP²_itþ

b

4FDI_itþ

b

5RE_it þ

b

6HC_itþ

b

7INS_itþ

q

1ðFDIINSÞ_itþ

l

iþε_1;it

(2)

ED_it¼

b

0þ

b

1ED_it1þ

b

2GDP_itþ

b

3GDP²_itþ

b

4FDI_itþ

b

5RE_it þ

b

6HCþ

b

7INS_itþ

q

2ðFDIHCÞ_itþ

l

iþε_1;it

(3)

ED_it¼

b

0þ

b

1ED_it1þ

b

2GDP_itþ

b

3GDP²_itþ

b

4FDI_itþ

b

5RE_it þ

b

6HC_itþ

b

7INS_itþ

q

3ðREINSÞ_itþ þ

l

iþε_1;it

(4)

ED_it¼

b

0þ

b

1ED_it1þ

b

2GDP_itþ

b

3GDP²_itþ

b

4FDI_itþ

b

5RE_it þ

b

6HC_itþ

b

7INS_itþ

q

4ðREHCÞ_itþ þ

l

iþε1;it

(5)

where ðFDIINSÞ is the interaction term between FDI and institutions,ðFDIHCÞrepresents the interaction between FDI and human capital, ðREINSÞ refers to the interaction between renewable energy consumption and institutions, whileðREHCÞ denotes the interaction between renewable energy consumption and human capital. Based on above-mentioned equations, the marginal effects of FDI and renewable energy on environmental degradation can be calculated utilising the following partial derivations:

vEDit

vFDI_it¼

b

4þ

q

1INS_it (6)

vEDit

vFDI_it¼

b

4þ

q

2HC_it (7)

vEDit

vRE_it¼

b

5þ

q

3INS_it (8)

vEDit

vRE_it¼

b

5þ

q

4HC_it (9)

To assess the signiﬁcance of these marginal effects on environmental degradation, we compute the new standard errors for the marginal effects by applying the procedure as suggested in Wool- dridge [97]. If the marginal effects are statistically signiﬁcant, we can conclude that the effects of FDI and renewable energy on environmental degradation are contingent upon the quality of institutions and or human capital.

3.2. Data and descriptive statistics

This study uses 5-years average unbalanced panel data derived from 114 countries spanning the 1986e2015 period. Basically, the general structure of our panel data is large in cross-section (N¼114) and small in timeframe (range fromT ¼3 toT ¼6).

Data related to per capita CO2emissions (environmental degradation), per capita real GDP, FDI, and the renewable energy consumption were collected from the World Development Indicators (WDI) made available by World Bank. The index of institutional quality was compiled from the International Country Risk Guide (ICRG) database. Finally, the average years of schooling data were extracted from the dataset of Barro and Lee.Table 1summarises the

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descriptive statistics and the unit of measurement of the variables.

3.3. Dynamic panel generalised method of moments

Given that the structure of our panel data is large in cross- section (N) and small in time-scale (T), the dynamic panel Gener- alised Method of Moments (GMM) estimator was selected for application as it is well suited to estimate our empirical model. The GMM approach was originally proposed by Holtz-Eakin, Newey and Rosen [98]; before Arellano and Bover [19]; Arellano and Bover [99]; and Blundell and Bond [20] further extended the estimator for panel data analysis. The GMM estimator’s advantage resides in the fact that it helps to remove country-speciﬁc effects as well as overcome endogeneity constraints, which could plausibly weaken the accuracy of our estimated results. Therefore, our environmental degradation model can be re-written as follows:

ED_it¼

d

1ED_it1þ4Witþ

l

iþεit (10)

wherein ED_itdenotes environmental degradation, W_itis a vector of explanatory variables,lirefers to time-invariant country-specific effect, and εit, the error-term. As the country-specific effect is assumed to be time-invariant, one can easily eliminate the effect by determining thefirst difference. Thus, the transformed model can be written as:

D

^EDit¼

d

1

D

^EDit1þ4

D

^Witþ

D

ε_it (11) Althoughﬁrst differencing successfully eliminates the country- speciﬁc effect, the inclusion of a lagged dependent variable, ED_it1is likely to cause endogeneity problems due to the existence of a correlation between ED_it1¼ ðED_it1ED_it2Þ and Dεit ¼

ðεitεit1Þ. To address this problem, Arellano and Bond [19] proposed the use of the lagged level variables with respect to the

moment conditions of

E½ðED_itsÞðDεitÞ ¼0 fors 2 wheret¼3;…;T and E½ðW_itsÞðDεitÞ ¼0 fors 2 wheret¼3;…;T as the instrumental variables. This approach, known as the difference GMM (FD-GMM) estimator was nevertheless deemed to be problematic as Blundell and Bond [20] argued that lagged level variable(s) are weak instruments if the variables in question behave persistently.

Thus, in an effort to improve the estimation’s efficiency and reli- ability Arellano and Bover [99] and Blundell and Bond [20] introduced the System-based GMM (SYS-GMM) estimator by using the first difference lagged variables as instruments with respect to moment conditions of E½ðDÊDitsÞðliþεitÞ ¼0 fors¼1 and E½ðD^WitsÞðliþ εitÞ ¼ 0 fors¼ 1. The validity of the selected variables can be examined using the Hansen’s (1982) J-test. In addition, the autocorrelation test of Arellano and Bond [19] will be used to affirm that the model is free from the second order of autocorrelation.

4. Empirical results

As presented in the earlier section, the dynamic panel GMM estimator is utilised to estimate the global environmental degradation model due to its advantages in addressing endogeneity issues as well as due to its generation of robust results. Nevertheless, the GMM estimator is divided into thefirst difference GMM (FD- GMM) estimator as introduced by Arellano and Bond [19]; and the System-based GMM (SYS-GMM) estimator as extended by Blundell and Bond [20]. The Monte Carlo simulation results of Blundell and Bond [20] disclose that the FD-GMM estimator is less efficient if the series is persistent, and especially when the estimated coefficient for the lagged dependent variable is large and near unity. In such circumstances, the SYS-GMM estimator is relatively more robust and reliable. To choose the best estimator for our study, we adopt the strategy proposed by Bond [100] to estimate our baseline model as presented in Equation (1) and the estimation strategy of the present study is summarised inFig. 1.

This essentially involves using three different approaches, namely the Ordinary Least Squares (OLS), the Fixed Effect (FE) and also the FD-GMM estimators. Using Bond [100] as our referential, if the FD-GMM is an efﬁcient estimator, then the size of the estimated coefﬁcient for ED_it₁should fall between the estimates of OLS and FE because the former is biased upward while the latter is biased downward, as attested by Nickell [101]. If such is not the case, the SYS-GMM estimator would be the preferable option. The estimation results and diagnostic tests are presented inTable 2.

Table 2indicates that the modelfitted the data well, as the results of the Wald test illustrate. In addition, the Arellano-Bond test for autocorrelation also suggests that the model is free from the second order of autocorrelation. Nonetheless, the Hansen-Jtest for the validity of the instrumental variable is statistically significant at the 5% level, implying that the FD-GMM estimation is biased downward as the estimated coefficient for ED_it1 is much lower than the FE estimation. These results indicate that FD-GMM estimator is inefficient. Therefore, we proceed to estimate the environmental degradation model with the SYS-GMM estimator with the resultant data illustrated inTable 3.

Overall, we have estimatedfive different models where Model 1 is the baseline model. Before we examine the estimated co- efficients, it is important to pay attention to the outcomes of our diagnostic tests to ensure that the estimated models and the results are reliable for interpretation purposes. In general, wefind that the results of all diagnostics tests applied are satisfactory. In terms offit, the results show that the Wald test is statistically significant at the 1% level for all models, inferring that the modelsfitted the dataset well. Moreover, wefind that the Arellano-Bond test for second- order of autocorrelation and the HansenJ-test for the validity of instruments did not reject the null hypothesis at the 5% significance level. It can thus be concluded that the estimated models are free from autocorrelation as well as endogeneity problems.

Table 1

Summary of descriptive statistics.

Variables Unit of measurement Mean Std.

Deviation

Min Max

Environmental degradation Metric tonnes of CO2 4.63 4.81 0.02 28.93

Per capita real GDP US$ (2010¼100) 13730.89 18120.03 158.63 106479

FDI % of GDP 4.53 11.70 2.9915 222.01

Renewable energy % of total energy consumption 32.59 28.92 0.01 97.98

Human capital Average Years of Schooling 7.96 2.92 0.89 13.57

Institutional quality Scaled from 0 to 50 30.34 8.93 4.91 49.14

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After establishing satisfactory diagnostic results, we begin our discussion of results for the baseline model. In general, all explanatory variables are statistically significant at the 10% level or better. More specifically, ourfindings show that the estimated co- efficients for GDP_itis positive while GDP²_itis negative and they are statistically significant at the 5% level. These results reiterate the non-monotonic inverted-U shape relationship between output and environmental degradation as expressed in the EKC hypothesis a finding that is consistent with that of previous studies (e.g.

Refs. [6,7,9,102,103]. Our estimated results also afﬁrm the pollution

haven hypothesis as we found that FDI has a significant positive impact on environmental degradation, afinding which is in accord with that of other studies [36,38,42,83]. In other words, the influx of FDI does not entail the transfer of green technology which would enhance environmental quality. Griffin and Enos [104]; in elucidating this phenomenon, observe that foreign investors are often exploitative rather than altruistic in technology transfer matters as their primary aim is to maximise profits through resource extraction and utilisation rather than assisting the host economies’ environmental conservation efforts. Apart from this, the variables of renewable energyðRE_itÞand human capitalðHC_itÞappear to be negative and significant in our estimated results implying that countries with higher usage of renewable energy or a higher level of human capital are more likely to control environmental degradation and safeguard its quality, afinding which is consanguineous with that Bhattacharya, Churchill and Paramati [105]. Nevertheless, wefind that institutions have a positive effect on environmental degradation, probably due to its impact on economic development.

Turning to the models with multiple interaction terms (i.e.

Model 2 to Model 5), we found that the results are fairly similar, especially regarding the impact of output, FDI, renewable energy and human capital on environmental degradation. In addition, all these variables are also statistically significant at the conventional level. Nonetheless, environmental degradation seems unresponsive to a change in institutional quality, as the variable remained statistically insignificant even at the 10% level. Furthermore, the four interaction terms namely,ðFDIINSÞ,ðFDIHCÞ,ðREINSÞand ðREHCÞare also insignificant as shown inTable 3. However, it is pertinent to point out that this low significance does not imply that the variables have not interacted as the given standard errors for interaction terms, including for the relevant individual variables, may be subject to multicollinearity issues [106]. Hence, it is essential to assay the marginal effects of FDI and renewable energy on environmental degradation based on a different level of institutional quality and human capital. The calculated marginal effects are as illustrated inTable 4.

Our estimated results show that the marginal effects of FDI on Fig. 1.Summary of estimation strategy.

Table 2

Estimation results of OLS, FE and FD-GMM.

Variables OLS FE FD-GMM

ED_it1 0.8782*** 0.2991*** 0.1682

(0.0163) (0.0334) (0.1709)

GDPit 0.5102*** 1.8810*** 1.7942***

(0.0749) (0.1969) (0.4804)

GDP²it 0.0281*** 0.0889*** 0.0772***

(0.0042) (0.0116) (0.0248)

FDIit 0.0332 0.0639** 0.0559*

(0.0235) (0.0309) (0.0338)

Renewable energy;RE 0.0022*** 0.0089*** 0.0126***

(0.0005) (0.0015) (0.0031)

Human capital;HC 0.0004 0.0150* 0.0123

(0.0048) (0.0089) (0.0095)

Institutions;INS 0.0063*** 0.0034 0.0033

(0.0017) (0.0024) (0.0023)

Constant 2.3151*** 9.4691*** e

(0.3565) (1.9414) Diagnostic tests

Wald test [p-values] 5910.21***

[0.000]

149.45***

[0.000]

400.50***

[0.000]

AR(2) test [p-values] 0.86

[0.390]

HansenJ-test [p-values] 18.89**

[0.026]

Note:***p<0.01,**p<0.05 and*p<0.10. Figure in the parenthesis (.) indicates the standard errors. OLS¼Pooled Ordinary Least Squares, FE¼Fixed Effect (within estimator), and FD-GMM¼First Difference Generalised Method of Moments.

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environmental degradation are positive at the lower levels of institutional quality and human capital with the effects being significant at the 5% level. This affirms that FDI has interacted with institutional quality and human capital in explaining environmental degradation. These results highlight that countries with poor institutional quality, due to endemic corruption and lax rules and regulations, are likely to attract FDI inflows that ultimately harm the host economies’ environmental quality. Likewise, our findings also suggest that countries with a low level of literacy tend to attract low-quality FDI inflows which are debilitating to environmental conservation and protection.

Analysing the marginal effects of renewable energy (RE) on environmental degradation, weﬁnd that the marginal effects of RE are consistently negative and signiﬁcant, regardless of the levels of institutions and human capital. These results indicate that better quality institutions ensure the use of more environmentally production processes, the induction of green technology and the enforcement of anti-pollution measures that collectively reduce environmental degradation. Likewise, a higher level of education

would enhance knowledge and innovation capabilities that would result in the adoption of systems, processes and technologies that are environmentally friendly.

5. Conclusion and policy recommendations

While the relationship between global environmental degradation and economic growth has been investigated extensively in the literature, the role of indirect (moderating) effects of the quality of institutions in reducing environmental degradation has not been examined. In this study, we ask an important question as to whether there exist additional effects (indirect/moderating), from having quality institutions in reducing environmental degradation.

In particular, we were interested to see whether the quality of a country’s institutions has an impact on FDI, human capital and renewable energy in addition to its direct impact in lowering environmental degradation. To the best of our knowledge, this issue has not been examined when studying the determinants of environmental degradation as the previous studies focused primarily Table 3

Results of Arellano-Bond dynamic panel system GMM estimation.

Variables Model 1 Model 2 Model 3 Model 4 Model 5

ED_it1 0.4247*** 0.4901*** 0.4216*** 0.4352*** 0.4455***

(0.1141) (0.1006) (0.1263) (0.1169) (0.1049)

GDPit 0.9602*** 1.1078*** 0.9841*** 1.0234*** 0.9395**

(0.3079) (0.3186) (0.3587) (0.3424) (0.3874)

GDP²_it 0.0372** 0.0474*** 0.0385* 0.0419** 0.0382*

(0.0176) (0.0183) (0.0200) (0.0187) (0.0213)

FDIit 0.0602* 0.1057** 0.0707* 0.0598* 0.0585*

(0.0313) (0.0478) (0.0381) (0.0360) (0.0354)

Renewable energy;RE 0.0123*** 0.0104*** 0.0120*** 0.0116*** 0.0119***

(0.0030) (0.0029) (0.0035) (0.0033) (0.0032)

Human capital;HC 0.0251*** 0.0347*** 0.0289*** 0.0249** 0.0264***

(0.0089) (0.0112) (0.0010) (0.0111) (0.0101)

Institutions;INS 0.0042* 0.0045 0.0049 0.0051 0.0046

(0.0024) (0.0031) (0.0031) (0.0035) (0.0032)

FDIINS e 0.0072 e e e (0.0051)

FDIHC e e 0.0102 e e (0.0155)

REINS e e e 0.00004 e (0.0001)

REHC e e e e 0.0002

(0.0004)

Constant 4.6339*** 5.2909*** 4.7707*** 4.8808*** 4.4013***

(1.4217) (1.4691) (1.6613) (1.6371) (1.8020)

Diagnostic tests

Wald test [p-values] 333.80***

[0.000]

399.56***

[0.000]

354.38***

[0.000]

374.48***

[0.000]

385.12***

[0.000]

AR(2) test [p-values] 0.60

[0.546]

0.75 [0.451]

0.59 [0.558]

0.89 [0.376]

0.86 [0.391]

HansenJ-test [p-values] 14.93 [0.312]

13.26 [0.428]

17.35 [0.184]

14.98 [0.309]

21.46 [0.123]

NT 477 481 479 474 476

N 112 112 112 112 112

No. of Instruments 21 22 22 22 24

Note:***p<0.01,**p<0.05 and*p<0.10. Figure in the parenthesis (.) indicates the robust standard errors.

Table 4

Marginal effects of FDI and renewable energy on environmental degradation.

Institutional qualityðINSÞ Human capitalðHCÞ

Min Mean Max Min Mean Max

FDI 0.0705** 0.1116 0.2461 0.0616** 0.0103 0.0674

(0.0306) (0.1207) (0.2164) (0.0296) (0.0995) (0.1852)

RE 0.0118*** 0.0128*** 0.0136** 0.0121*** 0.0135*** 0.0146***

(0.0032) (0.0038) (0.0053) (0.0031) (0.0036) (0.0049)

Note:***p<0.01 and**p<0.05. Figure in the parenthesis (.) indicates the robust standard errors.

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on the direct impact of institutions on global environmental degradation. This establishes the need for our study and its novelty to the existing literature. To examine this issue, we propose an extended version of the EKC model to analyse the potential indirect effects using 5-year average unbalanced panel data sets from 114 countries spanning the 1986e2015 period. Moreover, the dynamic panel GMM estimator is used to estimate the model. We use per capita CO₂emissions as a proxy measure of environmental degradation, which is estimated as a function of per capita real GDP, FDI, the renewable energy consumption, an index of institutional quality and the average years of schooling. Our models show that the estimated coefﬁcients are statistically signiﬁcant at the 5% level.

For GDP_it, the coefficient is positive while that for GDP²_it, is negative. These results validate the non-monotonic inverted-U shape relationship between output and environmental degradation as postulated by the EKC hypothesis. Our baseline model includes foreign direct investment (FDI), consumption of renewable energy (RE), human capital (HC), and institutional quality (INS). Our estimated results support the existence of the pollution haven hypothesis, wherein wefind that FDI has a significant positive impact on environmental degradation. In contrast, RE and HC have a negative impact while INS has a positive effect.

Furthermore, our study adds to the existing literature by suggesting the contingency effects on environmental degradation wrought by the institutional quality and the level of human capital in the countries examined. This importantfinding provides a fresh perspective on the complex relationships between FDI, renewable energy, institutions and human capital, and degradation of the environment. More pertinently, it provides insights and inputs for policymakers to enable them to formulate effective policies aimed at reducing such degradation. Our results indicate that effective institutions improve environmental quality by reducing FDI inflows that are reliant on obsolete production methods and technologies and by encouraging the use of renewable energy and green technology. In addition, the level of human capital has a major influence on FDI inflows and renewable energy use. This is because an educated workforce is more likely to demand governments to play a proactive role in ensuring the entry of quality FDI leveraging on green technology, renewable energy and sophisticated production processes and systems that are environmentally friendly and sustainable.

Our estimated results show that the marginal effects of FDI on environmental degradation are significant and positive at the minimum levels of institutional quality and human capital. This finding is significant as it implies that countries with poor quality institutions are likely to attract FDI flows which ultimately will harm environmental quality as the absence of stringent regulations, a weak political will to protect the environment, and the prioriti- sation of GDP growth over environmental conservation will collectively accelerate the exploitation of natural resources and the depredation of the environment by foreign investors looking to maximise profits. Likewise, ourfindings also suggest that countries with a low level of literacy rates would also attract the influx of FDI because the labour cost is relatively cheap. Perusing the marginal effects of renewable energy (RE), wefind that the effects on environmental degradation are consistently negative and significant, regardless of the level of institutions and human capital. As such, we can say that the improvements in institutional quality will by default lead to enhanced growth and a cleaner environment. The findings in this paper seem to suggest that improving government effectiveness, strengthening the rule of law and bolstering the regulatory structure to incentivise growth while deterring corruption could be the springboard for better economic growth and environmental quality.

In light of these findings, policymakers should realise that fostering the inflow of poor quality FDI as a short term fix for economic malaise may engender long term environmental problems that would burden future generations. It is only growth built on the solid foundations of good institutions and quality human capital that is worth pursuing as such growth is both economically sustainable and environmentally protective as a combination of rising incomes and quality human capital will serve as catalysts for the emergence of a virtuous circle of enhanced GDP, higher per capita incomes and better quality of life within a balanced and harmonious natural ecosphere. In other words, the one-fits-all policy of more FDI and renewable energy consumption to improve the quality of the environment does not necessarily hold.

The pre-requisite conditions of a high level of institutional quality, as well as education level, seem essential for inward FDI and renewable energy consumption to be known as effective policies which preserve the environment. Therefore, governments should devote more resources to maintain regulatory quality and corruption control, enforce the rule of law, and sustain governance effectiveness to be able to achieve both growth and clean environment.

Declaration of competing interest

The authors (Chor Foon Tang, Salah Abosedra and Navaz Naghavi) declare that they have no known competingﬁnancial interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.

Acknowledgement

We would like to thank the three anonymous reviewers for their constructive comments and suggestions to improve the earlier draft of this research paper.

Appendix. List of countries under review

No. Country No. Country No. Country

1. Albania 39. Guyana 77. Niger

2. Algeria 40. Haiti 78. Norway

3. Argentina 41. Honduras 79. Pakistan

4. Armenia 42. Hong Kong 80. Panama

5. Australia 43. Hungary 81. Papua New Guinea

6. Austria 44. Iceland 82. Paraguay

7. Bangladesh 45. India 83. Peru

8. Belgium 46. Indonesia 84. Philippines

9. Bolivia 47. Iran 85. Poland

10. Botswana 48. Iraq 86. Portugal

11. Brazil 49. Ireland 87. Romania

12. Bulgaria 50. Israel 88. Russian Federation

13. Cameroon 51. Italy 89. Saudi Arabia

14. Canada 52. Jamaica 90. Senegal

15. Chile 53. Japan 91. Singapore

16. China 54. Jordan 92. Slovakia

17. Colombia 55. Kazakhstan 93. Slovenia

18. Congo, Dem. Rep. 56. Kenya 94. South Africa

19. Congo 57. Korea (South) 95. Spain

20. Costa Rica 58. Kuwait 96. Sri Lanka

21. Cote d’Ivoire 59. Latvia 97. Sudan

22. Croatia 60. Liberia 98. Sweden

23. Cyprus 61. Lithuania 99. Switzerland

24. Czech Republic 62. Luxembourg 100. Tanzania

25. Denmark 63. Malawi 101. Thailand

26. Dominican Rep. 64. Malaysia 102. Togo

27. Ecuador 65. Mali 103. Tunisia

28. Egypt 66. Malta 104. Turkey