Ensuring environmental inclusion in developing countries: the role of macroeconomic policies

Abstract

In every society, there exist disadvantaged groups who have failed constantly to take part in the development of the economy and reap the benefits of economic growth as well. Along with economic and social factors, environmental factors are also accountable in making inclusion a challenge for the marginalized group. Contaminated drinking water, inappropriate sanitation systems, and pollution are the factors that affect health and wellbeing of the poor class by affecting their productivity. Thus, the lack of a clean environment leads the poor section towards further poverty and income inequality. Since the 2030 Agenda for Sustainable Development emphasizes three components to achieve sustainable development, namely economic, social, and environmental, this study inspects the role of macroeconomic policies in ensuring an inclusive clean environment in developing countries. Moreover, it considers the composite effect of fiscal policy and monetary policy on environmental inclusion by including interactive terms. This investigation uses FE-2SLS on a panel of 51 developing countries for the period of 1995–2019 to analyse the impact of macroeconomic policies on environmental inclusion. The study provides empirical evidence that fiscal and monetary policy has the potential to ensure an inclusive clean environment in developing countries. The findings imply that the macroeconomic policy actions depend on each other. Furthermore, governments in developing regions are required to cut nondeveloping expenditures and use expansionary monetary policy to promote green growth.

Introduction

While running on the path of economic growth, many individuals lag in the race due to a lesser number of opportunities available to them and thus have been thrown into a passive position. There are always individuals or groups in a society that are unable to engage in economic growth and hence do not benefit from it. Environmental factors, in addition to economic and social ones, are to blame for the marginalized minority’s difficulties assimilating into mainstream society. According to the report by the WHO (2014), between 2030 and 2050, climate change may cause approximately 250,000 deaths each year. On the other hand, contaminated drinking water, inappropriate sanitation systems, and pollution are the factors that affect the health and well-being of the poor class. It is observed that 1 out of 4 people do not have access to safe drinking water whereas almost half of the global population lacks access to safely managed sanitation facilities. At the start of the COVID-19 pandemic, three out of ten individuals across the world were unable to wash their hands with soap and water in their own homes.1 The sixth goal of SDGs emphasizes improving access to safe drinking water and sanitary facilities for all particularly vulnerable groups. Environmental justice is required in every economy because environmental ills affect unprivileged groups disproportionately. According to Inclusion Cornwall,2 natural environment helps to engage marginalized and isolated people; thus, environmental inclusion is necessary to make them participate in the growth process. It is a concept that focuses on not just only reducing pollution’s impact on the poor but also promoting fair access to nature for all.

The inverted U-shaped connection between environmental degradation and per capita income is explained by the Environmental Kuznets curve (EKC hereinafter), which states that environmental quality deteriorates during the early stages of economic development but improves in tandem with rising levels of economic success (Grossman and Krueger 1991; Stern et al. 1996). Environmental circumstances, on the other hand, affect economic growth, and poor environmental quality hinders many individuals from contributing to the growth process by reducing their productivity. The poorer sectors of the economy are more vulnerable to the negative consequences of environmental degradation, therefore providing a safe and green environment for everyone ensures that everyone has an equal opportunity to participate in the economic process. The human body’s exposure to harmful particles in water and air causes a variety of ailments, which reduces workers’ productivity by harming their health. People living in rural areas mostly rely on carbon-emitting energy sources, such as solid fuels which affect their health and reduce their productivity and cause an increase in health expenditures (Ahmad et al. 2021a).

The macroeconomic policy instruments can play their role in ensuring an inclusive environment. The developing economies have been using fiscal instruments just to raise revenue figures or to facilitate investor class so that a heave in economic growth could be realized. On the other hand, the government’s regarding its spending schemes has social and environmental outcomes as well. The increased government spending results in an increase in aggregate demand which stimulates the economic activities and use of natural resources. The excessive use of fuel, coal, and natural gas as sources of energy is needed to accomplish the growth targets which results in environmental degradation. The consequence of government spending on the environment also depends on whether the economy perceives the environment as a public or a luxury thing (Yilanci and Pata 2021). If the environment is perceived as a public good, environmental degradation will occur as a result of economic activities because market participants will not consider environmental aspects while carrying out market activities, and then the role of government to protect the environment through environmental regulations and pollution taxes becomes relevant (Hua et al. 2018; Moshiri and Daneshmand 2020). Alternatively, environmental deterioration hinders economic development (Ahmad et al. 2021b).

Monetary policy measures also affect the environment by affecting aggregate demand and corresponding market activities. The interest rate management ability of monetary policy contributes to a smooth interest rate over the economic cycles and thus brings about consistency and sustainability in economic growth. According to Qingquan et al. (2020), a country’s monetary policy is aimed at controlling inflation and sustaining long-term interest rates, and the changes in interest rate affect the industrial use of energy, aggregate demand, and financial development which eventually results in pollution. Monetary policy can also affect the well-being of economic agents through the channel of credit supply. The availability of credit on easy terms facilitates investors to move towards new and innovative projects. If enterprises are charged a higher credit rate for using innovative technologies, fewer new businesses would be started. However, rising CO₂ levels may occur from the growing usage of obsolete and less environmentally friendly technologies. Conversely, it influences welfare projects, such as basic water and sanitation infrastructure, through the interest rate channel.

Since achieving a sustainable environment is the seventh Millennium Development Goal (hereinafter MDG) and the 2030 Agenda for Sustainable Development emphasizes three components to achieve sustainable development, namely, economic, social, and environmental, this study inspects the role of macroeconomic policies in ensuring an inclusive clean environment in the developing countries. At first, we planned for the study to include all developing countries throughout the globe; however, data availability issues forced us to leave out several countries. By creating an index of inclusive clean environment for developing countries worldwide, this study adds to the current literature. The second aspect of this investigation that makes it unique is that it looks at how both fiscal and monetary policies affect the positive environment. In addition, the analysis incorporates interactive factors to account for the combined effect of fiscal policy and monetary policy on environmental inclusion.

Literature review

Many economies have ignored environmental damage because of their desire for rapid economic expansion. It is well acknowledged that economic expansion and poverty have a detrimental impact on environmental quality (Costantini and Martini 2010). Many studies have used different proxies to measure environmental sustainability; for example, Ahmed et al. (2022a) use ecological footprint for environmental sustainability while Ahmed et al. (2022b) use CO₂ reduction for environmental sustainability. On the one hand, Can and Ahmed (2022) have used renewable energy as a proxy for sustainable development, while Ahmed et al. (2022b) used per labor aggregate output for sustainable development. For environmental degradation, Jiang et al. (2022), Rehman et al. (2022a. 2022d), and Adebayo and Rjoub (2022) used CO₂ emissions. While many studies in the literature have identified urbanization, energy consumption, financial development, ICT, and agricultural activities as determinants of environmental degradation (Batool et al. 2022; Zhao et al. 2022; Rehman et al. 2022b, 2022c), several studies in the literature have addressed the environmental issue in the context of inclusive development. Wan and Zhuang (2015) presented a survey report on inclusive growth in which the economic, social, and environmental elements of inclusive growth were explored. The environmental component takes into account environmental issues such as safe drinking water, sanitation, and clean air, all of which can influence unprivileged groups’ productivity and, as a result, their ability to participate in the growth process. Sanitation facilities, water supplies, greenhouse gas emissions, forest areas, and fossil fuel usage are considered indicators of the environmental dimension. The components were scored using the linear scoring technique, and the components were aggregated using the simple arithmetic mean.

The World Bank (1992) introduced the concept of the development-environment nexus which explains the two-way causal links between the two. According to some studies, economic progress has a progressive influence on environmental inclusiveness in terms of provision of the clean drinking water and sanitary facilities to masses; however, air pollution and sulphur dioxide emissions harm the environment (Shafik 1994). A report by the WHO (2010) emphasizes the matter that in developing countries the cause of young children’s mortality and chronic pulmonary diseases among adults is indoor combustion of fuels and being shortage of access to advanced energy resources. Thus, to have sustainable and inclusive economic growth, improvement, and maintenance of environmental quality is very essential. According to the United Nations (2018), emerging economies are facing the challenge of maintaining environmental quality and dealing with climate change in order to attain sustainability. Thus, the world’s attention is increasingly turning to environmental challenges coming from excessive resource use, mass production, diminished forests, and polluted seas, all of which have an impact on the climate (Zhang et al. 2022).

Many factors are responsible for environmental exclusion and among those factors; fiscal and monetary instruments have the tendency to affect the level of exclusion. Many researchers have suggested fiscal reforms to ensure a sustainable environment for all; for example, the resource allocation can be done to achieve environmental goals such as provision of drinking water and sanitation services and forest restoration and pollution can be controlled through the taxation (Lopez and Palacios 2010; Jones and Yoo 2011; Harris 2013; Callahan and Pisano 2014). Government spending affects the income distribution and social wellbeing of individuals and raises the public awareness about environmental challenges and thus affects the environmental quality (Halkos and Paizanos 2013). Schrecongost et al. (2020) worked on inclusive sanitation approach and suggested that there is need of public projects that are financially viable to pursue equitable, inclusive, and sustained services in urban areas. On the other hand, Sanogo (2019) argues that fiscal decentralization affects the provision of public services, for example, health, water, and sanitation in rural areas. However, to control air pollution, Kamal et al. (2021) suggest that entrepreneurs should be encouraged to adopt environmentally friendly technology through tax incentive policies while the carbon tax is found effective in reducing CO₂ emissions (Liu et al. 2017; Freire-González and Ho 2019; Ojha et al. 2020). However, the study of Halkos and Paizanos (2016) investigated the impact of both taxes and government expenditures on CO₂ emissions and concluded that deficit-financed government spending is more successful in improving the environment than deficit-financed taxes.

On the other hand, monetary policy also affects the level of inclusiveness through its channels and many researchers found a negative link between interest rate and environmental degradation (Alola 2019; Isiksal et al. 2019; Qingquan et al. 2020). Based on the association among environmental degradation and interest rate; many studies found a positive relationship between interest rate and environmental degradation that suggests lowering the interest rate to control environmental degradation (Muhafidin 2020; Åström et al. 2019; Pradeep 2022). The positive association emerges because lower interest rates relieve the private sector’s financial restrictions. This leads to more money going into carbon-efficient technology and environment-friendly projects which causes the environment to improve. While working on the same line, Chan (2020) does not find any empirical evidence of the relationship between monetary policy and the environment. Numerous economists used the term green financing to highlight the role of monetary policy in protecting the environment, that is, lowering the interest rate on green loans supports green financing and innovations that lead to improvement in environmental quality (Dikau and Volz 2021; Debrah et al. 2022; Desalegn et al. 2022). Many researchers have considered both fiscal and monetary policy instruments to analyze the effect of macroeconomic policies on the environment (Mughal et al. 2021; Mahmood et al. 2022; Zeraibi et al. 2022).

The past literature provides a sufficient amount of evidence on how fiscal and monetary policy affects the environment. Some of the studies have shown that expansionary macroeconomic policies affect the environment quality while the rest have shown the adverse effect of macroeconomic policies on the environment. However, very few studies have discussed macroeconomic policies in the context of environmental inclusion which is aimed at providing a suitable and green environment for all. Furthermore, the interaction influence of macroeconomic policies on environmental inclusion in emerging economies throughout the globe is a gap in the literature. As a result, this study fills a research vacuum by examining the combined influence of fiscal and monetary mechanisms on environmental inclusion.

Methodology for the construction of environmental inclusion

This section explains the indicators of environmental inclusion and the methodology to construct the index of environmental inclusion. The indicators of environmental inclusion include access to drinking water, sanitation, forest area, and less consumption of fossil fuel energy as these will make an improvement in the health conditions and reduce hazardous deaths. The majority of people living in rural regions lack access to water supplies and sanitary facilities and rely on fossil fuel energy, while the forest is fast diminishing as the pace of urbanisation rises, exposing the environment to pollution. The World Bank indicates that in 2019, around 90 percent of the population has access to water sources whereas 65 percent population has access to sanitation facilities. The proportion of the population having access to water and sanitation in low- and middle-income economies is 90 and 66 percent respectively whereas in rich countries the figure is around 99.5 percent for access to water and sanitation. According to a study by PCRWR,3 84 percent of the total population in Pakistan is deprived of safe drinking water. According to World Bank data, 1.91 percent of Pakistan’s total land area is still forested, although the proportion of fossil fuel usage is steadily growing.

As discussed earlier, this study assumes four indicators of environmental inclusion that measure the access of people to basic facilities that ensure a clean environment. To measure the first indicator, sanitation, WDI’s data on the percentage of the population having access to improved sanitation facilities has been used. The provision of these sanitation facilities helps to prevent human contact from human excreta, and the facilities include a flush system, ventilated improved pit latrine, and composting toilet, to measure water source data on the percentage of the population having access to improved drinking water sources released by the World Bank Data. It includes the provision of tap water connections on-premises, public taps, tube wells, boring water, protected springs, and rainwater collection. For the third indicator, forest area as a percentage of the land area is used. It is an indicator that mainly focuses on environmental sustainability. Forest area includes all types of terrain that have tree stands (either natural or planted) of at least 5 m in situ. It does not matter whether these tree stands are productive or not, but these do not include tree stands in agricultural production systems. The last indicator in this category is the consumption of fossil fuel energy. To measure this indicator, WDI’s data on the consumption of fossil fuel energy as a percentage of total energy use has been used. Consumption of fossil fuel energy includes the consumption of coal, petroleum, oil, and natural gas products.

Except for fossil fuel energy consumption, increasing values of all the indicators contributes positively towards the improvement of the environment; therefore, inverse of fossil fuel energy consumption is taken in this study. The summary of the descriptive statistics of the indicators is given in Table 1, whereas the preliminary analysis of pairwise correlations and Bonferroni level of significance are given in Table 6 in Appendix. The descriptive statistics in Table 1 show that the average forest area in the developing region from 1995 to 2019 is 346000 conversely the minimum value is 515. The consumption of fossil fuel energy in the developing world has reached 100 percent whereas the average consumption has been 61.426 percent. It is a good indicator that in developing regions the maximum value of access to water and sanitation is 100 percent but the average values of access to water and sanitation are 80.552 and 62.21 due to the reason that people in some of the developing countries, for example, Peru, Senegal, and Venezuela, do not have access to drinking water and sanitation facilities.

Table 1.

Descriptive statistics of the indicators of the environmental inclusion

Variable	Mean	Std. Dev	Min	Max
Forest area	346,000	773,000	515	5,300,000
Fossil fuel consumption	61.426	28.012	0.293	100
Access to water	80.552	18.872	9.13	100
Access to sanitation	62.21	28.758	1.781	100

Table 6.

Pair-wise correlation coefficients

	Forest area	Fossil-fuel consumption	Access to water	Access to sanitation
Forest area	1.0000
Fossil-fuel consumption	− 0.0750* (0.0610)	1.0000
Access to water	− 0.1701*** (0.0000)	0.7579*** (0.0000)	1.0000
Access to sanitation	− 0.2052*** (0.0000)	0.7020*** (0.0000)	0.8149*** (0.0000)	1.0000

*, **, and *** show 10, 5, and 1 percent levels of significance respectively

For the construction of the environmental inclusion index, this study uses PCA. Mooi et al. (2018) state PCA gives consistent information subject to significant correlations. After the normalization of indicators, PCA is employed, and the Eigenvalues and factor loadings are reported in Tables 7 and 8 in Appendix respectively. The Eigenvalue of the first component is greater than 1; therefore, this study considers the first component only. The factor loadings of the first component are used as weights, and then the indicators are multiplied by their respective weights. The KMO statistic is 0.7323 which indicates that our data is well-suited for principal component analysis. Employing relative weight for each of the indicators and the following step is aggregation, and the linear-sum-of weighted normalized indicators are applied for aggregation.

Table 7.

Eigen values of the components

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.520	1.520	0.630	0.630
Comp2	1.000	0.696	0.250	0.880
Comp3	0.304	0.128	0.076	0.956
Comp4	0.176		0.044	1.000

Table 8.

Factor loadings of each component

Variable	Comp1	Comp2	Comp3	Comp4
Forest area	− 0.044	0.997	0.060	0.022
Fossil fuel consumption	0.563	− 0.028	0.795	0.226
Access to water	0.590	0.055	− 0.194	− 0.782
Access to sanitation KMO: 0.7323	0.577	0.047	− 0.572	0.581

Model of the study

Environment inclusiveness refers to the situation where a clean and green environment is available to everyone irrespective of whether they live in a rural or urban area and their income status. A clean environment can be perceived as public good since it meets the characteristic of public good, that is, nonrivalry and nonexcludable. To make a clean environment available for all, the government can play its role through fiscal instruments. The pattern of taxation and government expenditures can be designed to achieve the goals of a green environment. Green tax causes many industrialists to employ production techniques that cause less harm to the environment. López et al. (2011) explain that government spending oriented on public and social goods reduces pollution because it leads to human capital that affects environmental quality positively whereas Mohammed Saud et al. (2019) are of the view that government expenditure can affect the environment through three effects, that is, scale, composition and technical and the first effect results in environmental degradation while other two effects put a positive impact on the environment. The projects on water and sanitation may improve access to a clean environment for the deprived section of the economy, while green tax promotes energy-efficient technology and discourages deforestation.

Monetary policy also has the tendency to affect the level of environmental inclusion. Faria (1998) explains that monetary policy can affect the environment by minimizing transaction costs. Based on the Environmental-Kuznets-Curve hypothesis, both monetary and fiscal policy can indirectly affect environmental degradation by affecting the level of economic activities and the effect varies according to the income; that is, the effect of these policies on environmental degradation is positive in low-income countries and negative for rich countries. Xiaocang and Yaorong (2007) confirm that monetary policy through the expansion of bank credit causes an increase in physical capital and income while affecting the environment negatively whereas Munasinghe and Cruz (1995) explain that macroeconomic policies through the inflation channel affect the poor and cause them to rely on marginal land resulting in deforestation. The increased consumer prices also make access to gas and electricity difficult for the poor; thus, they use fuel wood and animal dug as an alternative. Combes et al. (2015), on the other hand, investigate how, in developing countries where deforestation and seigniorage are the government’s revenue options, tight monetary policy through a reduction in seigniorage can have a negative effect on environmental quality by increasing the rate of deforestation to compensate for missing revenues. Monetary policy, particularly interest rate policy, may have an impact on environmental inclusion through influencing investment in fundamental human rights such as education, health, access to safe drinking water, and sanitation.

Considering the theoretical link between macroeconomic policies and the environment, this study analyzes the composite impact of fiscal and monetary policy on the environment using panel data. One of the advantages of using panel data is that it allows us to have a larger number of observations and thus more degrees of freedom, which can lead to meaningful conclusions (Raj and Baltagi 2012). It also allows the researcher to cope with heterogeneities across time and cross-sections. Hsiao (2022) also discusses several benefits, including reducing the influence of excluded variables and exploring dynamic interactions. Thus, we have the following model:

$${\mathit{EI}}_{\mathit{it}}={\sigma }_1+{\sigma }_2{M}_{\mathit{it}}+{\sigma }_3{F}_{\mathit{it}}+{\sigma }_4{Z}_{\mathit{it}}+{\sigma }_5{M}_{\mathit{it}}·F_{\mathit{it}}+v_i+{\lambda }_t+u_i$$ 1

In Eq. (1), EI is the index of environmental inclusion, M is the money supply, F is the fiscal policy instrument, that is, government expenditure, M·F is the interactive term, Z is the set of control variables, and v_i and λ_t are the cross country and cross-time effects respectively. In this research, the variables are expressed in log form. The data on all variables are collected from the WDI (World Development Indicators) database. The effect of monetary policy on environmental inclusion given fiscal policy thus can be explained as follows:

$$\frac{\partial EI}{\partial M}={\alpha }_2+{\alpha }_{5}F$$ 2

However, the conditional effect of fiscal policy on inclusive growth given the different levels of monetary policy is as follows:

$$\frac{\partial EI}{\partial F}={\alpha }_3+{\alpha }_{5}M$$ 3

As we are using panel data of 51 countries (a list of countries is given in Appendix Table 9) for the period 1995–2019, to handle the issue of endogeneity, this study employs the FE-2SLS technique while POLS is applied for robustness check. To test the validity of instruments in FE-2SLS, Hansen’s J test is used with the null hypothesis of “instruments are valid”. The FE-2SLS model assumes no autocorrelation and heteroscedasticity; however, the existence of heteroscedasticity and autocorrelation can affect the results. To avoid the problem of missing observations for many economic series, we have collected data from 1995 to 2019. Data on institutional quality has been collected from ICRG; data on the human capital index is taken from FRED, while data on the rest of the variables of the model is gathered from WDI.

Table 9.

List of countries

1	Algeria	14	Cote d'Ivoire	27	Israel	40	Peru
2	Angola	15	Dominican Republic	28	Jamaica	41	Philippines
3	Bangladesh	16	Ecuador	29	Korea Rep	42	Senegal
4	Bolivia	17	Egypt Arab Rep	30	Malaysia	43	South Africa
5	Botswana	18	El Salvador	31	Mexico	44	Sri Lanka
6	Brazil	19	Ethiopia	32	Mongolia	45	Thailand
7	Cameroon	20	Ghana	33	Morocco	46	Togo
8	Chile	21	Guatemala	34	Mozambique	47	Tunisia
9	China	22	Haiti	35	Namibia	48	Turkey
10	Colombia	23	Honduras	36	Nicaragua	49	Uruguay
11	Congo Dem. Rep	24	India	37	Pakistan	50	Venezuela RB
12	Congo Rep	25	Indonesia	38	Panama	51	Zambia
13	Costa Rica	26	Iran Islamic Rep	39	Paraguay

Results and discussion

Before moving to estimation, we have drawn a scatter plot of the environmental inclusion and government expenditure which is given in Fig. 1. The graph does not indicate a clear positive or negative relationship while it can be concluded that at low levels of government expenditures, environmental inclusiveness improves while at a higher level, environmental inclusion shows a decline. The nonlinear relation signifies that initially, an increase in government expenditures causes the provision of basic human needs, that is, water and sanitation but higher expenditures through increased economic activities increase the need for energy and pollute the air because developing countries cannot abruptly jump to advance techniques of energy efficiency and unwilling depends on fossil fuel energy consumptions. On the other hand, increased economic activities also reduce the forest area and hurt the green economy.

Fig. 1.

Environmental inclusion and government expenditures

The scatter plot of the relationship between environmental inclusion and money supply is given in Fig. 2. The graph shows a positive relationship between money supply and environment inclusiveness. An increase in the money supply by affecting the interest rate reduces the cost of production and encourages investors to use environment-friendly methods of production. The availability of credit for green financing also helps the economies to have a clean and inclusive environment.

Fig. 2.

Environmental inclusion and money supply

Initially, this study investigates the effect of fiscal and monetary instruments on inclusive environment separately in models 1 and 2 respectively, and then the interactive role of fiscal and monetary policy is considered in model 3. Model 1 examines the impact of government expenditures on environmental inclusion. Since the scatter plot exhibits a nonlinear relationship between environmental inclusion and government expenditures, we have included the square of government spending in the model (Table 2).

Table 2.

Impact of macroeconomic policies on environmental inclusion

	Model 1		Model 2		Model 3
	POLS	FE-2SLS	POLS	FE-2SLS	POLS	FE-2SLS
M			0.248*** (0.000)	0.231*** (0.000)	− 0.316 (0.355)	0.412 (0.390)
G	2.032*** (0.000)	2.500*** (0.000)			0.472 (0.642)	0.780* (0.071)
G2	− 0.404*** (0.000)	− 0.510*** (0.000)			− 0.111 (0.624)	− 0.56* (0.058)
G*M					0.447 (0.111)	− 0.111* (0.098)
G2*M					− 0.086 (0.156)	0.020* (0.081)
HCI	1.147*** (0.000)	1.091*** (0.000)	1.137*** (0.000)	0.968*** (0.000)	0.998*** (0.000)	1.038*** (0.000)
IQ	0.255*** (0.000)	0.255*** (0.000)	0.190*** (0.000)	0.340*** (0.000)	0.164*** (0.000)	0.102 (0.498)
Trade	− 0.197*** (0.000)	− 0.200*** (0.000)	− 0.184*** (0.000)	− 0.13*** (0.000)	− 0.189*** (0.000)	− 0.200*** (0.000)
Constant	− 4.789*** (0.000)	− 5.182*** (0.000)	− 2.465*** (0.000)	− 2.200*** (0.000)	− 2.065* (0.080)	− 4.522*** (0.000)
No. of obs	1147	936	1147	921	1147	876
No. of instruments		10		11		14
R-square	0.60	0.593	0.61	0.68	0.667	0.656
F-Stat	52.733*** (0.000)	176.100*** (0.000)	167.763*** (0.000)	192.360*** (0.000)	233.323*** (0.000)	187.894*** (0.000)
Hansen’s J test		0.008 (0.927)		2.513 (0.113)		0.315 (0.574)

Environmental inclusion index is a dependent variable; G specifies the government spending; G2 indicates the square of government spending; HCI show the human capital index; IQ demonstrates the institutional Quality, and Trade is trade openness. Hansen’s J test uncovers that the instruments are effective. Parentheses uncover the p values

*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively

Human capital and institutional quality, which served as controls in the POLS and 2SLS model, were shown to have a favourable effect on environmental inclusion, whereas trade had a negative effect. These findings are consistent with previous research. According to Southgate and Basterrechea (1992), human capital building and scientific base in rural regions contribute to environmentally sound economic growth, whereas regarding institutional quality, studies by Bhattarai and Hamming (2002) and Sulaiman et al. (2017) suggest that a better quality of institution improves environmental quality by reducing the rate of deforestation. Many studies, notably Hettige et al. (1992), imply that trade openness drives emerging economies to specialise in goods that demand more labour and natural resources, causing environmental harm.

Since model 1 involves the linear and square terms of government expenditure, we take the derivative of the Eq. 4 w.r.t government expenditure to analyze the ultimate influence of government expenditures on the environment inclusion and the derivatives are as follows:

$${\mathit{EI}}_{\mathit{it}}={\alpha }_0+{\alpha }_1{G}_{\mathit{it}}+{\alpha }_2{G}_{\mathit{it}}^2+{{\alpha }_3}^{{}^{\prime }}{Z}_{\mathit{it}}+\mathrm{vi}+u_{\mathit{it}}$$ 4

$$\frac{\partial EI}{\partial G}={\alpha }_1+2.{\alpha }_2{G}_{\mathit{it}}$$ 5

The derivative implies that the impact of G on EI is nonlinear, and it is conditional on the different levels of G; therefore, this study uses the 25^th, 50^th, and 75^th percentiles of government spending. Table 3 shows the impact of government expenditure on the environmental inclusion. The result of POLS indicates that low levels of government expenditures affect the environment positively and significantly, but at a high level, the effect of government expenditure on the environment becomes insignificant while the FE-2SLS model shows a significant positive role of government expenditures on environmental inclusiveness that turned out to be insignificant at the median level of expenditures while at a higher level, government expenditures contribute to environmental inclusion negatively. This finding is consistent with the findings of Lopez et al. (2011), who found that there are two types of government spending: government spending on public goods and government spending on private goods, the latter of which includes subsidies for fossil fuel production and energy consumption, and thus a higher proportion of private spending in total government spending contributes to the deterioration of environmental quality while an equal proportion of public spending does not seem to.

Table 3.

Impact of government expenditures on environmental inclusion

Govt. Expenditure	POLS	FE-2SLS
P25 = 2.337	0.067*** (0.000)	0.192*** (0.000)
P50 = 2.540	0.046*** (0.011)	− 0.014 (0.674)
P75 = 2.760	0.022 (0.361)	− 0.237*** (0.000)

*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively. P25, P50, and p75 are the 25th, 50th, and 75th percentiles

To evaluate the role of the money supply in advancing environmental inclusiveness, model 2 examines the impact of the money supply on environmental inclusion and the results are reported in Table 2. Results indicate that trade is affecting the environment negatively in the case of the 2SLS model while the POLS model shows that trade affects environmental inclusion negatively. The models of POLS and 2SLS show a positive and significant impact of money supply on environmental inclusiveness, and this result is in accordance with the study of Moran and Queralto (2018) that emphasizes that monetary policy by lowering the cost helps firms to make innovations and also motivates the organizations to initiate the welfare projects and for marginalized groups and help them purchase energy-efficient technologies whereas energy efficiency results in an eco-friendly environment (Riti and Shu 2016), while Chishti et al. 2021 have found that expansionary monetary policy affects the environment negatively.

To assess the interactive role of fiscal and monetary policy on environmental inclusion, we have included the interactive term and the results of model 3 are also reported in Table 2. Results indicate that human capital exerts a positive impact in promoting environment inclusiveness whereas, except the 2SLS model, the coefficient of institutional quality is positive and significant in all models. The results of the POLS and 2SLS model show that trade openness affect the environmental inclusion of economic growth negatively. To analyze the impact of money supply given different levels of government expenditures and the impact of government expenditures at different levels of money supply, the derivatives of the model for money supply and government expenditures are given as follows:

$${\mathit{EI}}_{\mathit{it}}={\beta }_0+{\beta }_1{M}_{\mathit{it}}+{\beta }_2{G}_{\mathit{it}}+{\beta }_3{G}_{\mathit{it}}^2+{\beta }_{4}M{\ast G}_{\mathit{it}}+{\beta }_{5}M\ast G_{\mathit{it}}^2+{\beta }_6^{{}^{\prime }}{Z}_{\mathit{it}}+\mathrm{vi}+u_{\mathit{it}}$$ 6

$$\frac{\partial EI}{\partial M}={\beta }_1+{\beta }_4{G}_{\mathit{it}}+{\beta }_5.G^2$$ 7

$$\frac{\partial EI}{\partial G}={\beta }_2+{\beta }_{3.}2.G_{\mathit{it}}+{\beta }_4.M_{\mathit{it}}+{\beta }_{5}2.{M\ast G}_{\mathit{it}}$$ 8

The derivative of Eq. 6 w.r.t M shows that the impact of money supply on environmental inclusion is conditional on the levels of G, while the derivative for G indicates that the effect of government expenditures on the environmental inclusion depends on the levels of government spending and money supply. To examine the ultimate effect of the money supply given government spending, the 25^th, 50^th, and 75^th percentiles of government expenditures have been considered and the results are given in Table 4. The results of POLS and 2SLS models show a positive and significant effect of money supply on environment inclusiveness at all levels of government expenditures; however, higher levels of government expenditures reduce the effectiveness of money supply because now the role of the money supply is to correct the distortionary effects of fiscal policy measures and finance the expenditures. This finding is consistent with López et al. (2011) research, which contends that a high amount of government spending causes the scale effect and the income effect, both of which have a detrimental impact on the environment.

Table 4.

Impact of money supply given government expenditures

Govt. expenditure	POLS	FE-2SLS
P25 = 2.337	0.280*** (0.000)	0.279*** (0.000)
P50 = 2.540	0.263*** (0.000)	0.278*** (0.000)
P75 = 2.760	0.261*** (0.000)	0.279*** (0.000)

*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively

To evaluate the impact of government expenditures given money supply on environmental inclusion, the 25^th, 50^th, and 75^th percentile levels of the money supply are used for analysis and the results are given in Table 5.

Table 5.

Impact of government expenditures given money supply

Money Supply	POLS	FE-2SLS
G = P25 = 2.337
P25 = 3.438	0.112*** (0.005)	0.154*** (0.000)
P50 = 3.798	0.128*** (0.001)	0.152*** (0.000)
P75 = 4.118	0.142*** (0.001)	0.149*** (0.000)
G = P50 = 2.540
P25 = 3.438	− 0.052 (0.154)	− 0.042 (0.304)
P50 = 3.798	− 0.049 (0.103)	− 0.041 (0.179)
P75 = 4.118	− 0.045 (0.145)	− 0.041 (0.192)
G = P75 = 2.760
P25 = 3.438	− 0.23*** (0.000)	− 0.254*** (0.000)
P50 = 3.798	− 0.24*** (0.000)	− 0.251*** (0.000)
P75 = 4.118	− 0.25*** (0.000)	− 0.248*** (0.000)

*, **, and *** indicate the significance level at 10%, 5%, and 1% respectively

Results show that a low level of government expenditures at all levels of money supply affects environmental inclusion positively whereas the impact of median-level expenditure on the environment is insignificant on the other hand high government expenditure is observed to affect environmental inclusion negatively at all levels of the money supply. This result is in contrast with the study of Ercolano and Romano (2018) who observed that government spending affects the environment positively in EU countries while the finding of this study is in accordance with the view of Bernauer and Koubi (2006) that highlight that an increase in the size of government expenditure affects the environment negatively. The increasing proportion of government expenditures on subsidies given for fossil fuel production and energy consumption increases carbon emission and leaves the government of developing countries with fewer funds to spend on basic facilities like access to safe drinking water and basic sanitation facilities which eventually affects the environment inclusion negatively.

Conclusion and policy recommendations

An inclusive environment means a clean, healthy, and green environment irrespective of whether they belong to an urban or rural area. This study inspects the role of macroeconomic policies in ensuring an inclusive clean environment in the developing countries and considers the composite impact of fiscal policy and monetary policy on environmental inclusion by including interactive terms. The study uses FE-2SLS on a panel of 51 developing countries to analyze the impact of macroeconomic policies on environmental inclusion. The findings show that the macroeconomic policy actions depend on each other. Based on the findings, we conclude that low levels of government spending positively influence environmental inclusion whereas high levels of government spending adversely affect environmental inclusion since it is the content of government spending that counts rather than the magnitude of spending. Spending more on private goods, such as subsidies for fossil fuel consumption and energy consumption, has a negative impact on the environment; however, education expenditures help people understand the importance of environmental quality, while welfare projects improve access to water and sanitation for the marginalized group.

An increase in the money supply also helps to improve the inclusive environment because through interest rates and credit channels it encourages firms to make innovations and invest in R&D so that energy-efficient and environment-friendly production techniques could be adopted. The money supply is observed to affect environmental inclusion positively given low levels of government expenditures because, at high levels of government expenditure, the scale effect emerges and reduces the positive effect of the money supply.

1. Since results show that monetary policy affects environmental inclusion significantly, for an inclusive clean and green environment, this study suggests that microfinance schemes should be promoted in developing countries because the extension of credit to low to moderate-income groups to help them to start environment-friendly small-scale projects will help to ensure an inclusive clean environment in the region. Also, the monetary authorities should provide funds at lower interest rates for low-carbon projects.

2. Since high levels of expenditures are found to affect environmental inclusion negatively, therefore, instead of increasing overall expenditure, the government in developing countries should spend more on environment-friendly sectors including education, health, water, sanitation, and green transportation and infrastructure.

3. Regarding coordination of fiscal and monetary policy, expansionary monetary policy given to low- to median-level government spending is found to be an effective strategy for environmental inclusion; therefore, governments in developing regions are required to cut nondeveloping expenditure and use expansionary monetary policy to promote green growth.

Future research may focus on investigating the effect of disaggregated government consumption on environmental inclusion. Furthermore, instruments of monetary policy can be used to clearly understand the channel of monetary policy through which it affects the environmental inclusion.

Author contribution

Zakia Batool is responsible for the conceptualization, investigation, methodology, formal analysis, visualization, and writing the original draft. Arshad Ali Bhatti and Abdul Rehman are assigned to the supervision, investigation, visualization, review, and editing and made suggestions for the manuscript.

Data Availability

Data available on request from the authors.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.