Do institutional quality and military expenditure of G20 countries affect green investments?

Abstract

With the increase in awareness of environmental issues, companies and governments try to review their environmental policies. One of the most important policies for protecting the environment is the use of energy sources, which cause less or no harm to the environment. In this study, we examine the effect of institutional quality and the components of this institutional quality on green investments for G-20 countries. For this purpose, we employ static panel data analysis, as a main analysis, and dynamic panel data analysis as a robustness check. According to the main findings, institutional quality positively affects green investments and military spending reduces green investments. Also, robustness checks indicate that the effect of institutional quality and military expenditure of G20 countries on green investments is robust.

Keywords

Institutional quality green investments G-20 countries military expenditures

Introduction

The conflict between countries’ economic growth targets and environmental measures makes sustainable growth and green investments important issues.^1–3 In this context, major economies propose common policies to ensure sustainable growth and improve environmental quality.⁴ One of the policies proposed by the major economies is to channel the loans provided by the financial sector to green projects. The contradiction between economic growth and environmental sustainability may be managed through green investments.⁵ Thus, green finance and green investments become key factors worldwide.³

Green investment has a complex monitoring process, behavior, and challenges to succeed the ecological goals as well as economic goals^6–8 since the firms make green investments according to the nexus of size and the investment cost, the benefits for environmental sustainability,^9,10 and profitable conditions. The most potential advantage of green investment is to reduce the cost of carbon emissions by constructing more affordable and energy-efficient technologies.⁹ These situations bring to the agenda to determine the factors affecting green investments.

With the increasing interest in green finance and green investment issues, many studies focus on green finance. İlbasmis et al. ⁸ study the nexus of green finance investments and air quality, and Çitil et al. ⁶ research the relationship between green finance and corporate quality. İrfan et al.¹⁰ examine the relationship between green finance and green innovation and Lee and Lee¹¹ detect the effect of green finance on the green production process. Also, Zhou et al.¹² demonstrate the relationship between green finance and green growth, and Wang et al.¹³ research the relationship between green finance and renewable energy. Moreover, Jinru et al.¹⁴ study the economic effects of green finance and green logistics activities, and Liu et al.¹⁵ determine the nexus of green finance, fintech, and financial inclusion. In this context, the literature generally deals with issues such as green finance, energy, green growth, and green innovation.³ On the other hand, regarding green investments, Ren et al.,¹⁶ Musah et al.,¹⁷ Manurung et al.,¹⁸ Ye et al.,¹⁹ Li et al.,²⁰ Xiong and Sun,²¹ Meo et al.,²² Huang and Lei,²³ and Liao²⁴ mention the relationship between green investments and the environment. Tran et al.²⁵ reveal the link between green investments and sustainable growth. Zhao et al.²⁶ find the relationship between green investments, green growth, and innovation, and Azhgaliyeva et al.²⁷ study the relationship between green finance, green investments, and fiscal policies. In this context, examining green investments in-depth manner has great importance.

Institutional quality has a vast concept and includes some terms such as high quality, government regulation, services, and law individual rights, and these terms are effective factors in the environment.²⁸ Institutional quality has a key role in supporting countries to disseminate renewable technologies and green investment. Thus, institutional quality is recently seen as one of the drivers for renewable energy adoption.²⁹ The government supports renewable energy use with feed-in tariffs, carbon taxes, quota systems, premium payments, green investments, and a high degree of regulatory quality.³⁰ A strong rule of law is an indicator of institutional quality³¹ and countries, which have strong regulations for environmental rules, have higher institutional quality than countries, which have weak regulations for environmental rules.³² There are many studies, which examine the relationship between institutional quality and economic growth, institutional quality and financial development^35–40 institutional quality and environmental impacts.^38–40 However, to the best of our knowledge, there is no study, which addresses the relationship between institutional quality and green investments in the literature.

Global military expenditures nearly increased by 2.0 trillion dollars from 2001 to 2020.⁴¹ Political and economic changes, conflicts, and threats among the regional countries increase military expenditures.⁴² Gould⁴³ emphasizes that militarization is the type of expenditure, which intensely threatens the environment. While there is much research examining the relationship between military expenditures and environmental degradation^42,44–46 in the literature, however, there is no study on how military expenditures affect green investments to the best of our knowledge.

Mitigating and managing the effect of climate change brings intense challenges faced by humankind. Elimination of this challenge becomes important for the vast capital investments directed to renewable energy, energy efficiency, and green infrastructure projects.⁴⁷ Thus, the nexus between military expenditure and green investments is crucial for environmental sustainability and an in-depth understanding of green financing, the awareness of different green instruments, and the determining effective factors of green investments such as military expenditures are prerequisites for preventing environmental pollution.⁴⁸

The G20 countries approximately make global trade 80% and the world's economic output 85%. These countries’ military expenditures rapidly increase, and this group has significant responsibility for global security. The effect of the G20 country's military expenditures, which account for 82% of military expenditures in global military expenditures, on the international security environment is high.⁴⁹ Moreover, the most of G20 countries have financial challenges for sustainable social and economic development. These challenges are the lack of a common definition of green finance and international standards, the insufficient link between sustainable growth and national investment policy, the lack of regulatory association with green finance, ecological externalities, maturity mismatch, weak green project management; information asymmetry of capital markets, and wrong valuation of green projects’ risks.⁵⁰ Thus, Zhuang et al.⁵¹ emphasize the importance of institutional quality for G20 countries on the macroeconomic dynamics. The purpose of this study is to determine the effect of institutional quality and military expenditure of G20 countries on green investments. Within this scope, this study uses static panel data analysis as the main analysis and dynamic panel data as robustness checks and finds that institutional quality positively affects green investments and military expenditures reduce green investments.

This study has some contributions to the literature: There is a significant gap in the literature for the determining relationship between institutional quality and green investments, and this study tries to determine this relationship in the study. Institutional performance is not suitable for evaluation with a single variable, which is one of the major challenges for the countries. Institutional quality may be ambiguously measured by using a single variable.⁵¹ All indicators of governance, including political stability, government effectiveness, control of corruption, voice, accountability, and rule of law are considerably linked to each other. On the other hand, connecting all proxies of institutional quality into a single variable is a problem.⁵² There are six sub-components of institutional quality and this study creates separate models for institutional quality and each sub-component of institutional. In this context, this study obtains detailed findings for the relationship between institutional quality and green investments. In the literature, no study tries to determine the effect of military expenditures on green investments. In particular, the G-20 countries, which are the sample of the study, make high amounts of military expenditures, and the determination of the effect of military expenditures of these countries on green investments with the econometric method may help countries to review their military expenditure policies. Finally, this study makes robustness checks in addition to the main analysis since there is no study for the determining relationship between institutional quality and green investment, and this situation gives information about the robustness of the findings.

We organize this study into five sections. The second section includes the literature review and the third section gives data and methodology. The fourth section consists of findings and the final section represents the discussion and conclusion.

Literature review and hypotheses

We try to determine the relationship between institutional quality, military expenditures, and green investments with other independent variables such as carbon emissions, population, energy, consumption, economic growth, financial development, and education, which may affect the green investments line with the literature.^1,42 Thus, for the literature review, we examine the relationship between these variables and green investments in the study as well as the relationship between green investments, institutional quality, and military expenditure.

The relationship between green investments and institutional quality

Abid⁵³ and Xu et al.⁵⁴ state that the institutional quality of the country has great importance in promoting economic development and protecting the environment. The reason for this situation is that the government of countries with high institutional quality can directly or indirectly control economic development and environmental protection. Institutional quality can improve political stability, the rule of law, the quality of public services, less violence, and control mechanisms such as shaping the policies of government and the rule of law is particularly the most widely adopted element in governance and an important tool for environmental protection.⁵⁵ Institutional quality not only plays an important role in attracting foreign direct investments and in the economic development of the country but also contributes to the protection of the environment by encouraging green investments. Countries with institutional quality are in a more advanced position compared to countries, which do not have institutional quality in receiving green investments in many environmental issues such as environmentally friendly production, waste management, and improvement of efficient production processes. Although there is no study, which determines the relationship between institutional quality and green investments, there are many studies on external factors between institutional quality and environmental quality,^39,56–75 These studies show that there is a positive relationship between institutional quality and environmental quality and if the institutional quality is high, there may be higher environmental quality, more attention is paid to environmental regulations, and carbon emission is negatively related to institutional quality since the countries, which have regulations related to the international environmental standards, low bureaucracy, and low corruption, do not hesitate to force the rule. On the other hand, if the institutional quality is low, the environmental quality is low and carbon emissions are high since the firms make production by ignoring the environmental characteristics. In this study, the hypothesis to determine the relationship between institutional quality and green investments is given below:

H₁: There is a significant and positive relationship between green investments and institutional quality.

The relationship between green investments and military expenditures

Military expenditures, which are an important area for the defense of the country, on the one hand, make the countries safe regarding defense and on the other hand, they harm the environment.⁴² Military expenditure may affect the environment in two ways. The first is the destruction and pollution of the environment during the war period.⁴⁵ The second is the high amount of investments allocated from the budget to new investments in the non-war period. In this context, countries may put military expenditures to the fore and invest less in the environment.

Jorgenson et al.⁷⁶ determine that military expenditures have a positive effect on CO2 emissions. Erdogan et al.⁴² study Mediterranean countries and conclude that military expenditures increase CO2 emissions. Bildirici⁷⁷ finds that military expenditures increase CO2 emissions in the USA. Noubussi and Poumine⁷⁸ detect that military expenditures increase the emission of some gases, which are harmful to the environment. Qayyum et al.⁸² demonstrate that military expenditures negatively affect environmental quality both in the short and long term. Ahmed et al.⁷⁹ demonstrate that military expenditures increase environmental footprints in Pakistan and Clark et al.⁸⁰ claim that military expenditures increase energy consumption. From this point of view, the following hypothesis for the relationship between military expenditures and green investments is tested in this study:

H₂: There is a significant and negative relationship between green investments and military expenditures.

The relationship between green investments and carbon emissions

In the literature, there is the view that green investments have great importance in reducing carbon emissions⁸¹ and are used to reduce environmental pollution and carbon emissions by encouraging the funds needed. Sachs et al.⁸² argue that green investments should be increased and encouraged by creating bonds and green fund markets to achieve the sustainable development goals, which are among the United Nations’ 2030 goals. Noh⁸³ claims that green finance institutions should be established. Also, Azhgaliyeva et al.²⁷ suggest that private investments should be encouraged to promote an environmentally friendly economy. David and Venkatachalam⁸⁴ emphasize the public–private partnership of green investments. Sohag et al.⁸⁵ study Turkey and assert that military expenditures negatively affect environmental quality. However, Solarin et al.⁸⁶ show that military expenditures reduce the ecological footprints of the USA from 1960 to 2015 on the environment. From this point of view, the following hypothesis for the relationship between green investments and carbon emissions is tested in this study:

H₃: There is a significant and positive relationship between green investments and carbon emissions.

The relationship between green investments and population

The population may have a large impact on green investment, particularly in developing countries since land use and fuel consumption cannot be formally and accurately measured. Countries with rapidly increasing populations face a significant energy need with the growth of GDP and renewable energy may be needed to meet this energy need. This situation derives from fossil fuels, which are scarce and relatively expensive, while renewable resources are abundant. In addition, countries can encourage green investments to meet the energy needs, which arise with the increase in population due to the increase in gas emissions.⁹¹ Baldacci et al.⁹² predict that green investment and other investment equations have a positive relationship with the population. From this point of view, the following hypothesis is tested for the relationship between green investments and population is tested:

H₄: There is a significant and positive relationship between green investments and population.

The relationship between green investments and energy consumption

The production increases at the expense of completely consuming natural resources due to the industrial revolution.⁸⁸ This situation reveals the necessity of directing the energy required in production to alternative sources. Scarce energy resources are depleted in time and tend to run out completely shortly. Limited resources in nature, such as fossil fuels, will be depleted, but also cause great harm to the environment. Therefore, the importance of green investments increases since energy consumption increases and environmental pollution becomes a major problem. According to Zahan and Chuanmin,⁸⁹ green investments become an indispensable condition for achieving low environmental pollution and sustainable energy sources. Heine et al.⁹⁰ claim that green investment is an important factor in reducing pollution emissions, establishing an effective energy system, and creating climate markets. Li et al.⁸¹ assert that green investments have a significant impact on the clean energy revolution and private capital mobilization. Khan⁹¹ and Li⁹² emphasize the effective use of financial instruments such as green bonds starts to become a widespread tool. Thus, green investments may be adopted to increase energy consumption, which is used and needed by the world and can be financed more easily with green investments. In this study, the following hypothesis for the relationship between green investments and energy consumption is tested:

H₅: There is a significant and positive relationship between green investments and energy consumption.

The relationship between green investments and economic growth

Recently, the countries of the world aim for economic growth by increasing production and preventing environmental pollution. Thus, green investments come to an important position for countries in sustainable growth. Countries aiming for economic growth both support their economic growth and strive to create a sustainable financial structure by making green investments more attractive. According to Demena and Afesorgbor,⁹³ while many countries are trying to reduce environmental impacts on production, they try to encourage green foreign direct investments by focusing on economic growth. Soundarrajan and Vivek⁹⁴ demonstrate that green finance is one of the essential parts of low-carbon green growth since green finance links the financial industry, environmental recovery, and economic recovery reducing carbon emissions, improving the environment, and achieving sustainable economic growth. Soundarrajan and Vivek⁹⁴ claim that India has the opportunity to grow for financial sector by reducing the costs of environmental degradation. Noh⁸³ finds that green finance simultaneously tracks economic growth, environmental improvement, and the development of the financial sector. Doh⁹⁵ finds that green finance forms the infrastructure of green growth. In this study, the following hypothesis for the relationship between green investments and economic growth is tested:

H₆: There is a significant and positive relationship between green investments and economic growth.

The relationship between green investments and financial development

A developed financial structure is essential to increase and fully embrace green investments, which are extremely important for sustainable growth.¹⁰¹ Green investments may increase the environmental quality and orientation to sustainable resources and decrease carbon emissions since the financial structure develops the country's growth. Musah et al.¹⁷ assert that green investments need financial structure in Ghana. Shobande and Ogbeifun⁹⁶ claim that financial development encourages sustainable investments, which help improve environmental quality. Tamazian and Rao⁶⁸ support that financial development encourages investments in low-carbon projects and Li et al.⁹⁷ emphasize the encouraging green investment needed with research and development. In this study, the following hypothesis for the relationship between green investments and financial development is tested:

H₆: There is a significant and positive relationship between green investments and financial development.

The relationship between green investments and the education

Green finance is one of the basic conditions of sustainability and green investments. A high level of consciousness is required to achieve sustainable goals, and this high level of awareness may come to the agenda with high education. Therefore, there is a positive relationship between education and green investments. According to Ma,⁹⁸ higher education is the source of innovative human capital and a key factor for green economic growth. Alvarado et al.⁹⁹ find that human capital reduces the use of non-renewable energy and increases the use of renewable energy. Ma⁹⁸ examines the relationship between green finance, higher education, and green economic growth and asserts that higher education, green finance, and green economic growth are on the rise at the same time and the higher education index encourages the coordinated development of green finance and green growth. In this study, the following hypothesis for the relationship between green investments and education is tested:

H₇: There is a significant and positive relationship between green investments and education.

Data and methodology

This study aims to determine the relationship between institutional quality, military expenditures, and green investments. The sample of this study constitutes the G-20 country group and the data set covers the period 2004–2020. However, green investment data is not available for every G-20 country and G20 countries, whose data can be accessed are included in the sample. Therefore, the sample of the study consists of data from Germany, the USA, Australia, Brazil, China, Indonesia, France, South Korea, India, the United Kingdom, Italy, Japan, Canada, and Mexico. In addition, the reason for the analysis to start in 2004 is that the data obtained started from this date.

Green investments are seen in environmentally sensitive countries to prevent or stop environmental degradation. Environmental awareness changes with income level and economic development as emphasized by Grossman and Kruege^.¹⁰⁰ For this reason, the sample of the study consists of selected G-20 countries covering both developed and developing countries. To reveal the explanatory power of the main independent variables, some other independent variables such as carbon emissions, population, energy, consumption, economic growth, financial development, and education are included in the analysis by following the literature.^1,42 We create seven different models to detect how green investments are affected by institutional quality and the components of institutional quality. The variables and their abbreviations in the models are presented in Table 1.

Table 1.

Variables used in the analysis.

Variable	Abbreviation	Content of the variable	Source
Green investments	Lngreen	Expenditures for the transition to green energy	Bloomberg Database
Carbon emissions	LnCO₂	Amount of carbon per capita (Ton)	https://data.worldbank.or/
population	Lnpop	Total population
Energy consumption	Lnenercons	Total renewable and non-renewable energy Consumption per capita (Ton)
Economic growth	Lngdp	GDP
Military expenditures	Military	MilitaryEexpendtures/GDP
Financial development	Fdi	Loans given by banks to the private sector
education	Edu	education index	https://hdr.undp.org/
Institutional quality	Corqu	Components of institutional quality	The Worldwide Governance Indicators (WGI) project
Components of institutional quality	Voac	Accountability
	Polstb	Political stability
	Rlaw	The role of law
	Gofeff	Government effectiveness
	Regqu	Quality of regulations
	Concorr	Control of corruption

Driscoll-Kraay's¹⁰¹ estimator is used as the main analysis to determine the impact of G20 countries’ institutional quality and military expenditures on green investments in this study. This estimator is one of the static panel data analysis methods and is consistent with autocorrelation and varying variance problems Also, this study employs the dynamic panel data as robustness checks. To examine the stationarity of the variables is important to choose the right model and avoid situations such as spurious regression. In this context, this study analyzes the stationarity of the variables by using the Im-Pesaran-Shin¹⁰² and Harris–Tzavalis¹⁰³ unit root tests. We use the first-generation unit root test since there is no cross-dependency for models. However, existing the cross-sectional dependency in data shows that the second-generation panel unit root tests should be used¹⁰⁴ for determining the stationary level of variables. Thus, the cross-section dependency test is employed for making a decision on which generation unit root test should be used. After the stationarity analyses, the classical model needs to be tested against the fixed effects model. For this purpose, the F-test, which argues that the classical model is valid, is run. Then, this study performs the LM test, which tests the classical model against the random effects model.

If the results from the F and LM tests require rejection of the classical model choosing between the fixed effects and the random effects estimator is necessary. For this purpose, this study runs the Hausman¹⁰⁵ test and decides that the fixed unit effects or random unit effects estimator is the most accurate estimator with the assumptions of varying variance and autocorrelation. For other estimators, these assumptions are not tested. Finally, estimations are made after the choosing appropriate model. The following seven models using each institutional quality data presented in Table 1 are estimated.

Model 1:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} c o r q u_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(1)

Model 2:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} v o a c_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(2)

Model 3:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} p o l s t b_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(3)

Model 4:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} g o f e f f_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(4)

Model 5:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} r e g q u_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(5)

Model 6:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} r l a w_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(6)

Model 7:

L n G r e e n_{i, t} = a_{i, t} + β_{1} M i l i t a r y_{i, t} + β_{2} c o n c o r r_{i, t} + β_{3} l n C O_{2 i, t} + β_{4} lnenercon s_{i, t} + β_{5} lngd p_{i, t} + β_{6} f d i_{i, t} + β_{7} e d u_{i, t} + β_{8} l n p o p_{i, t} + μ_{i t}

(7)

Findings

Main findings

Before proceeding to the model determination and estimation, this study examines the summary statistics of the variables used in the models and Table 2 presents the main descriptive statistics of the variables. The number of observations in the sample varies between 238 and 224. This difference is due to some sections not having observations in certain periods. Also, the standard errors are generally less than zero and this situation is a desirable feature for consistent predictions.

Table 2.

Descriptive statistics.

Variable	Observation	Mean	Standard deviation	Minimum	Maximum
Lngreen	238	1.650	1.545	−3.218	5.2887
LnCO₂	238	20.539	0.984	19.438	23.09
Lnpop	238	18.666	1.184	16.817	21.067
Lnenercons	238	38.654	0.456	36.955	39.674
Lngdp	238	28.476	0.836	26.877	30.625
Military	238	1.747	0.905	0.311	4.922
Fdi	226	86.883	42.598	12.794	190.94
Edu	224	0.756	0.143	0.311	0.943
Corqu	238	0.6976	0.784	−0.831	1.676
Voac	238	0.677	0.764	−1.748	1.597
Polstb	238	0.139	0.639	−1.908	1.138
Gofeff	238	0.802	0.750	−0.483	2.006
Regqu	238	0.686	0.842	−1.074	1.933
Rlaw	238	0.668	0.899	−0.886	1.923
Concorr	238	0.623	0.927	−0.932	2.051

According to the findings of Table 2, foreign direct investment has the highest volatility and mean since G20 countries support the financial sector to fight against poverty and ensure sustainable growth¹⁰⁶ as expected. Thus, the values of the foreign direct investment may fluctuate. The second highest mean belongs to energy consumption since the energy consumption of G20 countries is approximately %85 of the global economy.¹⁰⁷ GDP and population follow energy consumption according to the size of the mean statistic. Also, the International Energy Agency¹⁰⁸ reports that the high level of CO₂ emission is relatively high in most of the G20 countries and CO₂ has one of the highest means in Table 2. After we present descriptive statistics, Tablo 3 reports correlations between the variables.

According to the findings of Table 3, there is a positive relationship between lngreen and lnpop, lngdp, fdi, Edu, and corqu. On the other hand, Table 3 presents the negative nexus between lngreen and lnCO₂ and military. To examine the stationarity of the variables, choosing the right model and avoiding spurious regression are important. In this context, the stationarity of the variables used in the study is analyzed with the Im-Pesaran-Shin,¹⁰² and Harris–Tzavalis¹⁰³ tests. Table 4 reports the findings of Im-Pesaran-Shin¹⁰² and Harris–Tzavalis¹⁰³ panel unit root tests.

Table 3.

Correlation matrix.

	lngreen	lnpop	lnco2	lngdp	military	fdi	edu	corqu
lngreen	1.0000
lnpop	0.3355	1.0000
lnCO₂	−0.5935	0.7430	1.0000
lngdp	0.7874	0.3736	0.7982	1.0000
military	−0.3660	0.1916	0.4746	0.5392	1.0000
fdi	0.3164	−0.3464	0.0528	0.2313	0.2058	1.0000
edu	0.2851	−0.6913	−0.1492	0.3030	0.2227	0.5274	1.0000
corqu	0.1943	−0.6975	−0.1976	0.2563	0.2684	0.5086	0.8783	1.0000

Table 4.

Panel unit root tests results.

Variables	Im-Pesaran-Shin Test	Harris–TzavalisTest
lngreen	−4.4162***	−4.7140***
military	−1.5163**	−1.6332*
corqu	−1.3886*	−1.1092*
voac	−2.2697**	−1.4835*
polstb	−4.3407***	−2.2615**
gofeff	−3.3863***	−2.2950**
regqu	−2.8920***	−2.5280**
rlaw	−2.8718***	−1.5502*
concorr	−1.6097*	−1.1887*
LnCO₂	−1.5548**	− 2.7179***
lnenercons	−7.6267***	−10.7487***
lngdpper	−1.9289**	−1.5454*
fdi	−2.1534**	−2.4150**
edu	−8.1009***	−7.8064***
lnpop	−7.0368***	−5.4354***

Note: ***,**, and * indicate the stationarity of the variables at 1%, 5%, and 10% significance levels.

According to the results of both Im, Pesaran, and Shin¹⁰² and Harris–Tzavalis¹⁰³ unit root tests in Table 5, all variables are stationary at the I(0) level. Also, Table 5 demonstrates the F-test results of seven models.

Table 5.

F-Test and cross-sectional test results.

	Model1	Model2	Model3	Model4	Model5	Model6	Model7
Coefficients	3.93(1.00)	4.12(0.000)	4.84(0.000)	4.03(0.798)	4.05(0.000)	4.37(0.000)	3.94(0.001)
All-FEs	0.80(0.046)	0.76(0.000)	0.85(0.069)	0.77(0.0198)	0.79(0.046)	0.81(0.003)	0.80(0.025)
FE year	2.23(0.005)	2.14(0.008)	2.54(0.001)	2.15(0.008)	2.28(0.004)	2.21(0.006)	2.22(0.006)
FE id	70.77(0.00)	72.93(0.00)	75.18(0.000)	73.74(0.000)	73.27(0.000)	74.12(0.000)	72.78(0.000)

Note: () shows probability values.

In Table 5, we test the classical model against FE-id constant unit effects, FE-year, constant time effects, and all-FEs constant unit-time effects, respectively. The probability value is less than 5% in all models and this finding means that the classical model is rejected against FE-id constant unit effects, FE-year, constant time effects, and all-FEs against constant unit-time effects. Thus, the fixed effects estimator against the classical model should include both unit and time effects and the result of the F-test reveals that the correct estimator is the fixed unit-time effects estimator. Table 5 gives the LM-Test results of seven models.

After testing the classical model against the fixed effects estimator, testing the fixed estimator against the random effects estimator is necessary. The results of the LM test, which tests the classical model against the random effects estimator, are reported in Table 6. The probability values of id-LM random unit effects, year-LM, random time effects, and id-year LM random unit-time effects are less than 5% for all models in Table 6. This result shows that the classical model is rejected against random effect estimators in all models. In addition, the results reveal that both random unit effects and random time effects are valid. Thus, the LM test shows that the random unit-time effect estimator is the correct estimator against the classical model. This study uses the Durbin-Watson test proposed by Bhargava, Franzini, and Narendranathan and the local best invariant test proposed by Baltagi-Wu to determine whether there is autocorrelation in the models. The autocorrelation is important if the test statistics are less than 2. The values are less than 2 in both tests and thus the autocorrelation problem is important for the fixed effects model in Table 6. Varying variance in the models is examined with the Wald test, which determines that the models have the problem of varying variance. This study analyzes the cross-sectional dependence of the models by using the Breusch and Pagan¹⁰⁹ LM test, the Friedman test, and the Frees test. According to the findings of these tests, there is no cross-section dependence in all three tests. Table 7 shows the results of the two-way Hausman test, which argues that the main hypothesis is a random effect estimator.

Table 6.

LM-test, autocorrelation, varying variance, and cross-section dependency test results.

Tests	Model1	Model2	Model3	Model4	Model5	Model6	Model7
id-year-LM	54.508(1.46e-12)	48.163(3.48e-11)	55.369(9.48e-13)	51.521(6.49e-12)	52.899(3.26e-12)	53.798(2.08e-12)	54.794(1.26e-12)
id-LM	50.691(5.41e-13)	44.030(1.62e-11)	51.214(4.14e-13)	47.633(2.57e-12)	48.996(1.28e-12)	49.848(8.31e-13)	50.908(4.84e-13)
year-LM	5.799(0.008)	8.259(0.002)	5.846(0.007)	4.395(0.018)	4.575(0.0162)	5.233(0.011)	5.543(0.009)
Baltagi-Wu LBI	1.218	1.232	1.222	1.218	1.228	1.212	1.234
Modified Wald Test	447.28(0.000)	636.62(0.000)	424.31(0.000)	375.84(0.000)	446.34(0.000)	431.44(0.000)	521.58(0.000)
LM Test	17.595(0.062)	17.525(0.064)	15.521(0.080)	17.650(0.058)	17.540(0.063)	13.210(0.095)	16.845(0.074)
Friedman Test	4.857(0.978)	4.400(0.986)	5.567(0.957)	4.857(0.978)	6.114(0.941)	3.829(0.992)	4.343(0.987)
Frees Test	0.105	−0.175	0.400	0.206	−0.025	0.215	0.069

Note: () shows probability values. Frees test critical values: %1 (0.4892), %5 (0.6860), %10 (1.1046).

Table 7.

Hausman¹⁰⁵ test results.

	Model1	Model2	Model3	Model4	Model5	Model6	Model7
$C h i^{2}$	18.05	19.45	23.09	17.27	18.91	21.02	17.34
Prob	0.0061	0.0035	0.0008	0.0083	0.0043	0.0018	0.0081

The probability values are less than 1% for all models in Table 7. This result shows that the basic hypothesis supporting the random unit-time effect estimator is rejected. Thus, the process of determining the appropriate estimator ends with determining that the correct estimator is the estimator with a constant unit-time effect. In the models, the problem of varying variance, autocorrelation, and cross-section dependence are identified. In this case, the fixed effects model estimator of Driscoll-Kraay,¹⁰¹ which makes efficient and consistent estimations with resistant standard errors even under the problems of varying variance, autocorrelation, and cross-section dependence, is used for model estimations investigating the relationships between variables.¹¹⁰ Thus, the present study employs the Driscoll-Kraay¹⁰¹ estimator, and the results of the estimator of Driscoll-Kraay¹⁰¹ are reported in Table 8.

Table 8.

Driscoll-Kraay¹⁰¹ estimator results.

Variables	Model1	Model2	Model3	Model4	Model5	Model6	Model7
military	−0.170**[0.073](−2.32)	−0.243**[0.081](−2.97)	−0.086[0.073](−1.17)	−0.128*[0.073](−1.75)	−0.156**[0.073](−2.13)	−0.203***[0.077](−2.62)	−0.131**[0.071](−1.84)
corqu	0.524***[0.188](2.79)	-	-	-	-	-	-
voac	-	0.434***[0.145](2.99)	-	-	-	-	-
polstb	-	-	0.414**[0.320](2.23)	-	-	-	-
gofeff	-	-	-	0.098[0.170](0.58)	-	-	-
regqu	-	-	-	-	0.515**[0.223](2.31)	-	-
rlaw	-	-	-	-	-	0.387**[0.145](2.66)	-
concorr	-	-	-	-	-	-	0.353***[0.122](2.89)
LnCO₂	−0.556***[0.170](−3.27)	−0.382**[0.194](−1.97)	−0.714***[0.162](−4.39)	−0.702***[0.165](−4.26)	−0.645***[0.164](−3.91)	−0.557***[0.171](−3.25)	−0.548***[0.170](−3.22)
lnenercons	0.181[0.118](1.53)	0.167[0.118](1.41)	0.217**[0.117](1.84)	0.224**[0.119](1.88)	0.187[0.119](1.57)	0.180[0.118](1.52)	0.192*[0.117](1.64)
lngdpper	1.454***[0.170](8.51)	1.492***[0.169](8.81)	1.419***[0.175](8.09)	1.506***[0.173](8.69)	1.418***[0.175](8.09)	1.508***[0.170](8.86)	1.442***[0.171](8.43)
fdi	0.009***[0.001](6.10)	0.011***[0.001](7.08)	0.008***[0.001](5.85)	0.009***[0.001](6.01)	0.009***[0.001](6.27)	0.008***[0.001](5.52)	0.008***[0.001](5.59)
edu	−0.161[1.615](−0.10)	0.047[1.153](0.03)	1.612[1.327](1.17)	2.006[1.552](1.29)	0.460[1.580](0.29)	−0.157[1.643](−0.10)	−0.083[1.580](−0.05)
lnpop	0.692***[0.208](3.31)	0.610***[0.213](2.87)	0.863***[0.212](4.06)	0.761***[0.211](3.60)	0.831***[0.209](3.96)	0.617***[0.215](2.87)	0.630***[0.212](2.97)
c	−49.226***[4.843](−10.16)	−52.007***[4.671](−11.13)	−50.776***[4.793](−10.59)	−52.097***[4.890](−10.65)	−49.791***[4.876](−10.21)	−49.160***[4.874](−10.09)	−48.278***[4.919](−9.81)
$R^{2}$	0.70	0.71	0.73	0.74	0.72	0.71	0.70
F	65.62	66.13	64.37	62.33	64.54	65.31	65.86
Prob.	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Note: [] is the standard error and () is the t-statistic. while ***,**, and * indicate 1%, 5%, and 10% significance levels.

The R² value of all models is over 70%, which is an acceptable level. The probability values and constant coefficients of the F statistic values are significant and negative and some variables have positive coefficients and some have negative coefficients. Population (pop), GDP per capita (gdpper), and foreign direct investment (fdi) is statistically significant with positive coefficients and at least a 5% significance level. According to these findings, the increases in population, per capita income, and foreign direct investments appear as factors that increase green investments. These results are in line with the findings of Ali et al.¹¹¹ The amount of carbon emission (CO₂) is statistically significant at a 1% significance level in all models, and there is an inverse relationship between carbon emissions and green investments as revealed in the previous studies.^81,112,113 Since green investments are investments that aim to reduce carbon emissions, this negative relationship between green investments and carbon emissions is an expected result. The energy consumption (enercons) and education (edu) variables are not statistically significant on green investments at 1%, 5%, and 10% significance levels.

The effect of institutional quality on green investments is another main motivation of this study. In the first model, which includes the general institutional quality indicator, the coefficient is positive, the level of significance is 1% level, and the increase in institutional quality in general increases green investments. In Model 2 of Table 8, we test the effect of freedom of expression and accountability component of institutional quality on green investments and obtain a statistically significant result at the 1% significance level. Thus, the development regarding freedom of expression and accountability supports green investments.

Political stability is one of the most important components of institutional quality and the present study uses this component in the third model. The coefficient of political stability is positive and significant at the 5% significance level. According to this finding, ensuring political stability increases green investments. Also, the present study uses government efficiency, which represents whether the military expenditures are rationally made in the budget balance and the prevention of expenditures in the fourth model. Although the coefficient of government efficiency is positive, making an inference about how the efficiency of the government affects green investments may be not possible since this coefficient is statistically insignificant.

This study includes the quality of regulation, which can be expressed as the ability of the government to understand and formulate high demands from individuals and the private sector and translate them into practices, in the fifth model. The coefficient of quality of regulation is positive and statistically significant at the 5% significance level. Thus, the improvement of the government's ability to understand the rising demands from different segments of society seems to increase green investments. The rule of law, when expressed as individuals’ and firms’ assurance of independent judgment, property rights, enforcement of contracts, and the prevention of inhuman practices such as torture, can be expressed as the component with the most potential to affect other organizational quality components. The rule of law is included in the sixth model. The results obtained are in line with most institutional quality components. The coefficient of the rule of law variable is positive and statistically significant at the 5% significance level. According to this finding, the institutionalization of the rule of law principle and its widespread adoption by society are the increasing factors in green investments. Finally, this study tests the effect of the anti-corruption variable, which is closely related to the rule of law, on green investments in the seventh model and the results show that the prevention of corruption increases green investments at a 1% significance level. Prevention of corruption can be expressed as the use of public or bureaucratic powers to obtain private/individual benefits.

Significance levels of military expenditures vary from model to model. Although the significance levels change, military expenditures have a negative effect by reducing the factor of green investments. Military expenditure includes new military technologies, and nuclear weapons, as well as expenditures on major infrastructure and construction activities. Testing of military technologies, mobilization, and training of military forces, necessary military infrastructure, and construction activities are huge expenditures. These activities hurt the environment in line with the findings of Gould,⁴³ Jorgenson & Clark,¹¹⁴ and Jorgenson et al.¹¹⁵ However, the estimations show that military activities do not only pollute the environment but also the expenditures made for the realization of these activities harm investments, which aim to prevent or reduce environmental pollution such as green investments.

To determine the negative impact of military expenditures on green investments is necessary. However, to determine how military expenditures for defense, attack, or other purposes, reducing green investments for environmental protection is also necessary. War periods are periods when environmental concerns are pushed into the background, as in many other issues. For this reason, it cannot be surprising if the military expenditures decrease in green investments as a natural consequence of the war period. The second type of periodic military expenditure is made when there is no war or security threat and may also result from the countries’ security strategies and military formations. However, the military developments in other countries may be more decisive in the second type of military expenditure. Therefore, the second type of military expenditures outside of war periods, as suggested by Erdogan et al.,⁴² shows geopolitical and regional characteristics. Geopolitical risks and regional military developments can affect the distribution of production resources in countries, forcing them to invest in new warfare technologies or to maintain existing military capabilities. This situation, which we can interpret as the exclusion effect, may lead to a decrease in the number of funds, which can be used for environmental investments. In addition, increased military expenditures may cause countries to be considered risky by investors making them reluctant to make green investments with long-term results. Thus, the inverse relationship between military expenditures and green investments becomes more significant.

Robustness checks

There is no study, which tries to determine the effect of institutional quality and military expenditure of G20 countries on green investments to the best of our knowledge. Thus, testing the robustness of the relationship between institutional quality, military expenditure, and green investments is important. For this purpose, we estimate the generalized method of moments (GMM) method developed by Arellano ve Bover¹¹⁶ as robustness checks since GMM eliminates estimation biases in addition to the joint endogeneity and correlated challenges¹¹⁷ and Table 8 presents the GMM findings.

According to the findings of Table 9, the previous period values of the green investments have a significant effect on the current values of the green investments and this situation shows the persistence of the green investments for the seven models. Also, the Autocorrelation (1) and Autocorrelation (2) findings of Table 9 indicate that there are no first and second autocorrelation problems for all models. Finally, the Wald χ2 test and Sargan test emphasize the significance of each model as a whole and the validity of the instruments, respectively. The military expenditures negatively affect the green investments in line with the findings of Table 8. Also, the institutional quality and the components of institutional quality have a positive and significant effect similar to the findings of Table 8. These findings prove that our main and robustness findings are similar to each other. In brief, the effect of institutional quality and military expenditure of G20 countries on green investments is robust. On the other hand, carbon emission hurts green investment for Model 1, Model 3, and Model 5, and GDP per capita has a positive effect on green investments except for Model 3 and Model 7. Moreover, foreign direct investment is a positive determiner for Model 1, Model 2, Model, 4, Model 6, and Model 7. However, education has a statistically significant effect on green investments for Model 4 and Model 5. The population and energy consumption have statistically negative, positive, and insignificant effects on the green investments for the models.

Table 9.

Arellano ve Bover GMM results.

Variables	Model1	Model2	Model3	Model4	Model5	Model6	Model7
lngreen₋₁	0.166[0.264](0.06)	−0.294[0.340](−0.87)	0.475**[0.224](2.12)	−0.409[0.401](−1.01)	−0.952[0.697](−1.37)	−0.164[0.393](−0.42)	−0.421[0.425](−0.99)
milexp.	−0.905*[0.548](−1.67)	−0.373*[0.101](−1.82)	−1.561**[0.668](−2.33)	1.282[1.225](1.05)	−0.868**[0.173](−1.93)	−0.153*[0.077](−1.63)	−0.376*[0.157](−1.84)
corqu	3.263*[1.450](1.65)	-	-	-	-	-	-
voac	-	1.258**[0.985](1.99)	-	-	-	-	-
polstb	-	-	2.038*[1.279](1.65)	-	-	-	-
gofeff	-	-	-	3.249*[1.235](1.77)	-	-	-
regqu	-	-	-	-	2.289**[0.123](2.31)	-	-
rlaw	-	-	-	-	-	1.210*[0.210](1.66)	-
concorr	-	-	-	-	-	-	2.978*[2.017](1.89)
LnCO₂	−1.106**[0.170](−2.57)	3.863[3.386](1.14)	−1.519**[0.171](−2.39)	8.917*[5.045](1.77)	−6.320**[1.074](−1.91)	4.303[3.237](1.33)	1.568[1.643](0.34)
lnenercons	0.832*[0.118](1.67)	−0.667[0.495](−1,35)	0.800*[0.312](1.84)	1.414**[0.649](2.18)	1.392***[0.510](2.73)	0.782**[0.368](2.12)	−0.506[0.335](−1.51)
lngdpper	13.027*[7.605](1.71)	10.083*[3.044](1.78)	6.916[4.862](1.42)	20.334**[9.379](2.17)	19.131**[8.106](2.36)	11.149*[6.805](1.65)	−1.993[2.166](−0.92)
fdi	0.018*[0.010](1.89)	0.019*[0.011](1.81)	0.009[0.007](1.34)	0.018**[0.009](1.99)	0.031[0.020](1.58)	0.020*[0.010](1.91)	0.023**[0.011](2.12)
edu	−41.347[31.655](−1.31)	−46.669[39.630](−1.18)	−21.931[22.519](−0.97)	−133.13**[53.794](−2.10)	−81.760**[40.057](−2.04)	−43.546[30.892](−1.41)	8.799[6.975](1.26)
lnpop	2.909*[0.870](1.70)	−4.229[9.150](−0.46)	−5.324[4.837](−1.35)	3.434[3.195](0.009)	−9.977**[3.462](−2.23)	−8.486*[1.549](1.65)	−6.166[5.563](−0.094)
c	104.184[112.006](0.93)	−227.41[251.047](−0.91)	−113.810[81.991](−1.39)	−626.65**[287.631](−2.18)	−145.652[65.478](−1.40)	159.850[154.874](0.390)	37.709[34.165](1.10)
Wald chi²Prob > chi²	58.88(0.000)	46.31(0.000)	68.32(0.000)	78.94(0.000)	50.84(0.000)	68.56(0.000)	61.43(0.000)
Sargan	5.444(0.928)	5.901(0.980)	5.492(0.910)	3.989(0.850)	2.751(0.658)	5.720(0.920)	4.136(0.890)
Autocorrelation (1)Autocorrelation (2)	−0.756 (0.447)0.061(0.950)	0.025(0.979)0.440(0.659)	−1.648 (0.099)0.389(0.697)	−0.587(0.121)0.619(0.535)	0.981(0.326)1.132(0.257)	−0.106(0.914)0.906(364)	−1.068(0.285)-0.306(0.759)

Note: [] is the standard error and () is the t-statistic. while ***,**, and * indicate 1%, 5%, and 10% significance levels.

Discussion and conclusion

This study aims to examine the impact of institutional quality and military expenditures on green investments between 2004 and 2020 for G-20 countries In this context, seven different models are estimated with a fixed unit-time effect estimator. As a result, this study estimates how green investments are affected by developments in military expenditures, institutional quality, carbon emissions, population, energy consumption, economic growth, financial development, and education.

This study creates seven models to examine the impact of institutional quality and military expenditures on green investments. According to the results, the coefficients of seven models for military expenditures are negative, and the increases in military expenditures have a reducing effect on green investments in the selected G-20 countries. Therefore, military expenditures are identified as activities, which directly pollute the environment in line with the literature,^76,80,114 and reduce green investments. Also, this result does not change in seven different models.

One of the main motivations of the study is to determine the effect of institutional quality on green investments, and this study finds that all institutional quality indicators increase green investments. The sub-components of institutional quality such as freedom of expression and accountability, political stability, regulatory quality, rule of law, and prevention of corruption positively affect green investments. Thus, improvements in freedom of expression, ensuring political stability, improving the quality of regulation, ensuring the rule of law, and reducing corruption increase green investments by protecting the environment. This result is important for policymakers since the decisions of policymakers may improve institutional quality and the results of the policies to be implemented affect green investments and the environment. In other words, the goal of reducing environmental pollution and correcting environmental degradation is a complex process that cannot be limited to measures and practices, which directly reduce carbon emissions such as banning the use of vehicles using fossil fuels. Institutional improvements are also part of this complex process. Therefore, the results for the relationship between green investments and institutional quality have quality contributions to the expansion of policy horizons of decision-making units.

The other main motivation of the study is the effect of military expenditures on green investments. This finding is an expected and not surprising result since green investments may decrease when security concerns take precedence over environmental awareness. However, considering the period and the country group of this study, the wars may cause a decrease in green investments. Also international formations including military activities and projections of the countries for the future cause an increase in military expenditures outside of war periods. Military expenditures of many countries are increasing in this context. The development and testing of new military technologies have a long funding process and cause more military expenditures by distorting fund allocation to the detriment of other investment expenditures including green investments. In addition, to fund allocation, military projections can also affect country risks and investments. Regarding the negative impact of military expenditures on green investments support these inferences.

The study also tests the relationship between carbon emissions and green investments. The relationship between changes in carbon emissions and green investments is negative in all models, as expected and this finding is a natural consequence of green investments to reduce or prevent carbon emissions. Another important result of the study is that environmentally sensitive developments are positively affected by income level. GDP and foreign direct investments are among the key variables for green investments. The increases in per capita income cause an increase in the level of welfare and environmental awareness. The positive effect of foreign direct investments on green investments is also emphasized in many other studies such as Alfaro et al.,¹¹⁸ Azman-Saini et al.,¹¹⁹ and Kurtishi-Kastrati.¹²⁰ This study makes an important contribution to the literature by determining the effect of military expenditures and institutional quality on green investments with other variables. The method of this study is not only for one country but also for the leading economies of the world. The military expenditures try to prevent environmental pollution by excluding or reducing green investments in selected G-20 countries and improvements in corporate quality indicators contribute to the reduction of negative pressure on the environment and sustainability by increasing green investments.

However, the findings of this study have some limitations, which are the data contained between the 2004–2020 period and the sample countries including Germany, USA, Australia, Brazil, China, Indonesia, France, South Korea, India, United Kingdom, Italy, Japan, Canada, and Mexico constitute. There are few studies on green investments in the literature. Therefore, testing green investments with different data sets, different country groups, and different variables may give valuable contributions to the literature. Also, examining the impact of institutional quality and military expenditures on green investments for NATO countries and Shanghai Five countries may be employed.

Footnotes

Declaration of conflicting interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Abdulkadir Barut

Festus Victor Bekun

Furkan Demirtaş He is currently pursuing his PhD programme in international trade. His research interests are climate change and foreign trade. He is a researcher who is keen on research and open to innovations.

Emine Kaya holds a doctoral degree in finance and her area of research is climate change finance, agricultural finance, and financial markets.

Festus Victor Bekun currently serves as an Assistant Professor at Istanbul Gelisim University in Istanbul, Turkey. He is also a Senior Research Fellow at Lebanese American University, Superior University Lahore, and University of Johannesburg in South Africa. Recently, Dr. Bekun has also been affiliated with the prestigious Lebanon American University in Beirut, Lebanon, as a Senior Research Fellow. He has published over 250 scientific papers in top-tier outlets indexed in Web of Science (WOS) and Scopus. Dr. Bekun has impressive H-index scores of 55 in Google Scholar, 54 in Scopus, and 55 in WOS. He is recognized as one of the global influential scientists by Stanford University, being among the top 2% influential scientists for 2021 and 2022 editions. Additionally, he has been recognized as a Highly Cited Researcher (HCR) by WOS.

Mücahit Çitil is currently working as an Assistant Professor at Harran University. He holds a PhD degree in economics. He has published more than 10 scientific articles in top-tier publications indexed in Web of Science (WOS) and Scopus.

Mustafa Torusdağ is currently working as an Associate Professor at Van Yüzüncü Yıl University. He holds a PhD degree in Economics. He has published a large number of scientific articles in top-level publications indexed in Web of Science (WOS) and Scopus.

Abdulkadir Barut received his PhD degree in finance. His research interests are green finance, green economy. He is currently working as an associate professor at Harran University. He has many publications based on WOS and Scopus.

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