Given the global nature of many environmental issues, this research investigates the relative importance of bank-based development in elucidating the heterogeneity in environmental pollution across three (3) income levels and six (6) regional characteristics. This study carefully sampled sixty-two (62) countries based on data availability from 1960 to 2020. A two-step system, Generalized Methods Moments (GMM was selected for the estimations. The result revealed that bank-based development has a direct influence on environmental pollution, indicating that higher bank-based development, hinders environmental quality. However, the turning point of the relationship finds a negative coefficient of bank-based development. Thus, at first, there is a significant rise in bank -based development practices cause a rise in carbon emissions, which begins to fall after a certain point. Varied results are produced when income levels and regional characteristics are considered. The unfavorable relationship between bank-based development and environmental pollution increases the need for severe pro-carbon emission policies and laws. While implementing these policies, carbon-reducing technologies that are environmentally beneficial in the financial sector should be considered. The research on the damage that bank-based development does to the environment is linked to the advanced economies and markets. Consideration of a study that combines a subset of the dataset with different income classifications and regional blocks enriches the existing body of knowledge.
Research Article
Improving environmental pollution through bank-based development: Does it work?
https://doi.org/10.21203/rs.3.rs-3240219/v1
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Two-step system GMM
Environmental pollution
Bank-Based Development
Ecologists, politicians, and government stakeholders worldwide have expressed worry over the rapid acceleration of environmental degradation and its influence on global warming. Gillis (2013) demonstrates that excessive fuel, petroleum products, and fossil fuel demand will add to global warming. warming and environmental degradation. Several attempts have been made worldwide during the past two decades to alleviate environmental degradation by reducing carbon dioxide (CO2) emissions. The efforts encompass political activities as well as financial and capital expenditures. The European Commission (2011) has initiated the Eco-Innovation Action Plan of the European Union through political initiatives. Through sustainable development goals, the European Union (EU) and the United Nations have worked to address environmental degradation in the majority of economies through sustainable financing or green finance. The initiative aims to achieve significant advances to the objective of sustainable development, which decreases ecological consequences and strengthens robustness to adverse environmental conditions. The impact of these efforts and activities remains limited and debatable.
Financial intermediaries (banks) play an importance in bolstering a nation's economy (Demirguii-Kunt and Levine, 2001). Since the eighteenth and nineteenth century, various arguments have been advanced by policymakers and economists concerning the relevance of bank-based development to the reduction of environmental pollution. This is premised on the fact that extended financial sector evolution and bank contestability offer a good accessible platform. for households and firms to have access to loans and advancements. Overall, these loans and innovations make it more easier for consumers to purchase CO2-emitting products and vehicles, such as refrigerators and air conditioners (Zhang, 2011). Bagehot and Schumpeter argued that a well-functioning banking system in the 19th century stimulated technological innovation and economic growth. The position of Bagehot (1873) was reinforced by Schumpeter (1934), who argued that banks played an important role in creating the industrial revolution. What isn't known and hasn't been investigated in the literature is how to measure the link between bank-based development and environmental degradation along these contributions. First, this study provides a new viewpoint on how bank-based development, economic factors, energy-related factor, investment factor, sociodemographic factor and political factor affect the environmental both short-term and long-term. Unlike the previous studies, the current study extends and introduces investment, sociodemographic, and political factors as part of the instruments for consideration. This is relevant because foreign direct investment (FDI) inflow industrial production improves in the manufacturing industries activity (Frankel and Romer, 1999). According to Dietz (2015), population growth, political, and societal factors influence carbon emissions. The categorization of the dataset into short-run and long-run is intended to assist in determining the stability and inelasticity of the study instruments' relationships The second significance of this study to the body of knowledge is the application of panel threshold analysis. This is introduced to investigate the relationship's turning point. Consequently, the quadratic term of the variable term non - linearity test is utilized. Third, the study is further discussed in line with the income levels classifications and regional characteristics. The dataset decomposition is necessary because natural resources, social, and economic developments are not the same across countries. Hence, the need to proffer policy advice in line with the income levels classifications and regional characteristics. Establishing this relationship is crucial because it has diverse implications for development policy (Calderón and Liu, 2003). Besides, while early studies have employed conventional regressions, this study adopts and estimates approaches employing panel data analysis estimation method such as two-step generalized methods moments (GMM). The existence of lagged dependent variables and nation-fixed effects presents a challenge that requires the application of more sophisticated econometric methodologies. The two-step system GMM estimator used within the investigation was developed for panel data sets with a constant T and a growing N as T increase. Time dummies and the t-statistics associated with regression are resistant to arbitrary heteroscedasticity and autocorrelation; as a result, the two-step system GMM estimator is used within the investigation. Recent research has shown that, despite the significance of the two-step system GMM estimator, in certain instances, the SGMM may be especially vulnerable to the poor instrument issue (Bun and Windmeijer, 2010.).Roodman (2009) concludes that the number of internal instruments grows in a quadratic way as the number of time periods goes up. Overfitting endogenous variables can happen when there are too many instruments, which can make coefficient estimates inaccurate and make the Hansen test of the instruments' joint validity less reliable. To help reduce these biases and focus on the long-term impact of the variables of interest in omitting short-term fluctuations in the datasets, the authors used a 3-year average to reduce the instrument count by ‘collapsing’ instruments and a reduction in the sample size. Finally, while earlier studies have been discussed and focused mainly on developed countries, little is known for combination of both developed countries and developing countries. According to our best knowledge, we have yet to come across any study that investigates this phenomenon with a global focus.
The result revealed that bank-based development exerts a positive effect on environmental pollution, demonstrating that greater bank-based development hinders environmental quality. However, the turning point of the relationship finds a negative coefficient of bank-based development. Consequently, in the early phase, a rise in bank-development activities corresponds to an increase in carbon emissions, which then begins to fall after a certain threshold is reached. Besides, for high-income economies, the result shows that a rise in bank-based development results in a rise in carbon emissions. Finally, it is worthy to note that in Europe and Central Asia North, energy usage, foreign direct investment, net inflows, and inflation have shown positive and statistically significant relationships to environmental pollution. The positive relationship established suggests banks’ domestic credit to consumers and the private sector increases environmental degradation or waste. Our findings have important implications for different stakeholders in enacting pro-carbon emissions policies at both bank and national levels.
Whereas the following section discusses the review of the literature and formulation of hypotheses, section 3 provides the study methodology. The findings from the research are discussed in Section 4, while explanation of results are discussed in Section 5.
Environmental innovation management is gaining importance in both academia and the business world (Hojnik and Ruzzier, 2016; Schiederig et al., 2012). Diversified forms of development that attempt to achieve substantial advancements toward the goal of sustainable development by reducing environmental impact and increasing resilience to environmental pressures have been pursued with some success. Following a substantial corpus of studies (Barbieri, 2016; Horbach, 2016), as well as the governmental actions of the Eco-Innovation Action Plan of the European Union (European Commission, 2011).
The challenge for governments is how best to manage the environment at a minimal economic cost without adversely affecting the objective of achieving higher economic growth. This study is based on two essential theories, namely technological diffusion, and knowledge spillover theories. The term "technological diffusion" refers to the process by which an innovation is communicated, selected, and adopted via various means over time by a group or organization. It was popularized by Everett Rogers in his seminal work, Diffusion of Innovations, published in 1962. The theory of knowledge spillover was initially attributed to Alfred Marshall and was later developed by Glaeser et al. (1991) The concept of knowledge spillover describes an exchange of ideas between individuals. Knowledge spillover theory has become important in research and technological development (RTD), which is when companies and governments try new things to make new products and services and improve ones that already exist. According to Carraro (2000), carbon emissions can be reduced by incentivizing firms to engage in research and development and technological innovation. Recent studies on environmental innovations, corroborated by to Carraro (2000), indicate that environmental knowledge spillover, technological diffusion, innovativeness, and appropriate policy design can support the greening of energy use (Aldieri and Vinci, 2020; Azul et al., 2020; Dogan et al., 2020; Sinha et al., 2019).
The empirical focus of this section remains on six strands of literature that summarize the strong correlation between economic growth, bank-based development, energy consumption, investment factors, socio-demographic factors, political factors, and environmental degradation. The study differs from earlier studies that has focused on economic growth and environmental degradation, bank-based development and environmental degradation (Adams and Klobodu, 2018; Farhani and Solarin, 2017; Shahbaz et al., 2015), energy consumption and environmental degradation (Ahmad et al., 2016; Akadiri et al., 2019). In Table I, the six types of empirical literature are presented.
Although the discussions regarding economic growth and environmental degradation are never-ending, the results are inconclusive.Rakshit and Neog (2021) investigate the effect of macroeconomic uncertainty on environmental degradation in India and conclude that macroeconomic uncertainty increases carbon emissions. Using Australian data,Balaguer and Cantavella (2018) demonstrate that economic growth significantly contributes to the decrease in CO2 emissions per capita. According toWang et al. (2020) investigation into the causes of environmental deterioration, economic growth, nonrenewable energy use, and urbanization have positive effects on the Ecological Frontier (EF) in the SSA countries.Farhani and Ozturk (2015) investigate the cause-and-effect link between real GDP and CO2 emissions in Tunisia and suggest a monotonic positive relationship between real GDP and CO2 emissions.Al-Mulali et al. (2015), utilizing data from 23 European countries, find a negative long-term relationship between GDP growth and CO2 emissions.Alvarado and Toledo (2017) used Johansen co-integration tests and ECM to prove that real GDP and vegetation cover in Ecuador have a negative relationship. Economic growth and urbanization are significant determinants of environmental degradation, according to a study byAdams and Klobodu (2018) employs a wide range of econometric techniques, such as cross-country regressions and the Generalized Method of Moments (GMM).Acheampong (2018) examines the dynamic interplay between energy use, carbon emissions, and economic growth in 116 nations and discovers that economic expansion has a detrimental impact on carbon emissions worldwide as well as in the Caribbean and Latin America.
Studies on the relative relevance of bank-based development on carbon dioxide pollution have been growing, with limited and inconclusive findings. Using a standard reduced-form modeling approach across Brazil, Russia, India, and China (BRIC) economies,Tamazian et al. (2009) note that a higher degree of financial development decreases environmental degradation. Talukdar and Meisner (2001) use both random effects and reduced-form models in 44 developing countries and indicate that the higher the degree of private sector involvement in a developing economy, the lower its environmental degradation. In exploring the relationship between financial instability and environmental degradation within the multivariate framework,Shahbaz (2013) shows that financial instability increases environmental degradation. Using a bootstrapping bound testing approach,Shahbaz et al. (2018) demonstrate that a bank-based development reduces carbon emissions, consequently enhancing French environmental quality. In a small emerging economy,Abbasi and Riaz (2016) employed the Augmented VAR approach, the Error Correction Model (ECM), and the Vector Error Correction Model (VECM) and observed that financial factors only played a role in emission reductions in the latter period, when liberalization and finance sector expansion were more prevalent. Other studies such asTamazian and Bhaskara Rao (2010), Adams and Klobodu, (2018) andRashid Khan et al. (2019) have all concluded that financial liberalization and domestic credit provided by the financial sector may be harmful to environmental quality if it is not accomplished in a sound regulatory structure.
The third strand of research is concerned with energy consumption and carbon emissions. Aye and Edoja, (2017) apply the panel causality method to 31 developing countries and conclude that energy consumption and population have a positive and statistically significant impact on CO2 emissions. Acheampong (2018) applies panel vector auto regression (PVAR) and system-generalized method of moment (System-GMM) and notes that energy consumption positively causes carbon emissions in MENA. Additional research has established a negative correlation between energy consumption and carbon emissions. Wang et al. (2020) combine 14 Sub-Saharan Africa (SSA) countries and establish that renewable energy consumption plays a negative role in the econological frontier. Using 23 selected European countries, Al-Mulali et al. (2015) indicate that renewable electricity has a negative long-run effect on CO2 emissions. In separate studies, countries such as India according to Alam et al. (2016) and Masoud and Hardaker (2012), financial variables play a significant role in environmental degradation.
The next strand of literature discusses the link between investment factors and environmental degradation.Huang et al. (2022) study the impacts of FDI inflows on carbon emissions using a feasible generalized least square (FGLS) for G20 economies and conclude that foreign direct investment (FDI) inflows are positively associated with carbon emissions. In India, when the autoregressive distributive lag bound testing model and Toda–Yamamoto causality approach were used,Rakshit and Neog (2021) report that FDI inflow positively influences the environmental quality of a host economy as FDI inflow sometimes brings green, efficient, environmentally-friendly technologies and better environmental-management practices. Alternatively,Frankel and Romer (1999) investigate the impact of trade on environmental pollution. Frankel and Romer (1999) suggest that higher FDI inflow in the industrial sector increases industrial activity, leading to increased environmental pollution. In China, Rashid Khan et al. (2019), who analyze government financial policies to support natural resource markets using a two-step GMM estimator, found that FDI inflows significantly influenced soft and hard commodity markets to reduce natural resource rents and agricultural and livestock production. According toShahbaz et al. (2018) FDI harms the ecology in France, which confirms the pollution-haven hypothesis.
In addition, socio-demographic factors and environmental degradation are investigated. According to Alam et al. (2016), countries such as Brazil, China, India, and Indonesia are experiencing rapid population growth. For India and Brazil, the correlation between CO2 emissions and population growth was identified to be statistically significant. Using five South Asian nations, Yang et al. (2021) conclude that population has a negative effect on CO2 over the long term.
Finally, studies have shown that political factors are considered to have influence on environmental degradation. Using a Hierarchical models to investigate political influences on greenhouse gas emissions from US states, Dietz (2015) concludes potential of politics and affluence have long been seen as major drivers of environmental stress. Benlemlih et al. (2022) examine 145 countries around the world and conclude that political stability, corruption, and women's participation in politics have a significant impact on CO2 emissions.
The strands of literature discussed above primarily examine the connection between economic growth, bank-based development, energy consumption, investment factors, socio-demographic factors, political factors, and environmental degradation given the international dimension of many environmental problems. The extant literature attempts to fill the gaps in which previous empirical studies have largely been ignored. First, we consider that this important relationship should be examined with an emphasis on the short-run and long-run effects using the two-step GMM technique. The decision to test for short-run and long-run effects helps policymakers establish the stability and inelastic relationship of the study instruments. Second, for essential policy implications, the inter-relationship of the variables is analyzed along regional and income level characteristics. These segmentations provide a new perspective on how income levels and regional issues affect environmental issues. Establishing this relationship is crucial because it has diverse implications for development policy (Calderón and Liu, 2003). Finally, the contribution of this study to the literature is the application of panel threshold analysis. This is introduced to investigate the turning point of the key instruments’ relationship.
[Place Table I here]
3.1. sample, data sources, and justification.
This study carefully sampled sixty-two (62) countries based on data availability from 1960 to 2020. These countries are further segmented into three (3) income level classifications and six (6) regional blocks. The uncommon characteristics of these selected countries merit their inclusion in the study. These selected countries have shown sustained welfare improvement [1]. Table II lists the definitions and data sources of all the variables. The sampled countries, income level classifications, and regional blocks are found in appendix A.
[Place Table II here]
3.2. Methodology
The study adopts and estimates models using dynamic panel data estimators. The existence of lagged dependent variables and country-fixed-effects acts as a threat, which requires the usage of other sophisticated econometric methods. The GMM estimators employed by the study are designed for the panel data set with fixed T and a large N as T increase. Time dummies and the t-statistics associated with regression are robust to arbitrary heteroskedasticity and autocorrelation; hence, GMM estimators were used in the study. Arellano and Bond (1991) introduced the first-differenced GMM (Dif-GMM) estimator, and Arellano and Bond (1998) developed the two-step system GMM. Even though the Dif-GMM estimator eliminates the endogeneity problem by using first differencing, it may be plagued by large finite-sample biases and poor precision when the time series are persistent (Blundell and Bond, 1998). However, a two-step GMM model was chosen for the estimations. The selection criteria included the following: First, the two-step system GMM model is based on a two-step estimator that allows for finite sample correction (Windmeijer, 2005). Second, the two-step system GMM eliminates some of the finite-sample biases and inaccuracies inherent to the Dif-GMM. Again, Blundell and Bond (1998) System GMM is preferred because it uses lagged levels of endogenous variables as instruments for equations in first differences. To help reduce these biases and focus on the long-term impact of the variables of interest by omitting short-term fluctuations in the datasets, the authors used a 3-year average to reduce the instrument count by ‘collapsing’ instruments and a reduction in the sample size
The general model used can be specified as:
$$C02\_E{M}_{it}={\beta }_{0}+{\beta }_{i}{\chi }_{it}+{\delta }_{i}{{\rm Z}}_{it}+{{\rm K}}_{i}+{e}_{it}$$
1
where \(CO2\_E{M}_{it}\) represents the CO2 emission (Metric tons per capita), \({\beta }_{0}\) is a constant term.\({\chi }_{it}\) is a vector of explanatory variables such as domestic credit to the private sector % GDP (DC_PS), Gross domestic product growth (GDP_G), trade openness and investment Economic(TOP_I), inflation, consumer prices (IFL_P), exchange rate (EXC_R), Energy use (kg of oil equivalent per capita) (ENE_U, foreign direct investment and net inflows (% OF GDP) (FDI_NI), population density (people per sq. km of land area) (POP_D) and government effectiveness (GOV_E) are all independent indicators. \({{\rm Z}}_{it}\) is a vector that captures control variables that were used in the study. \({{\rm K}}_{i}\) represents fixed effects and \({e}_{it}\) is the error term. These variables are group into the following proxies: banks' credit, Economic factors, energy-related factor, investment factor, socio-demographic factors, and political factor.
The two system GMM specifications are as follows:
$$CO2\_E{M}_{it}={\beta }_{0}+C{O}_{2}\_E{M}_{it-1}+{\beta }_{i}{\chi }_{it}+{\delta }_{i}{{\rm Z}}_{it}+{e}_{it}$$
2
where \(CO2\_E{M}_{it}\) represents the CO2 emission (Metric tons per capita), \(C{O}_{2}\_E{M}_{it-1}\) for one-period lagged \(CO2\_E{M}_{it}\),\({\beta }_{0}\) is a constant term.\({\chi }_{it}\) is a vector of explanatory variables, \({{\rm Z}}_{it}\) is a vector of control variables, and \({\epsilon }_{it}\) is the error term.
A formal expression for the relationship to be estimated is given as:
The study investigates the quadratic term of the variable's nonlinearity. Alternately, the defining moment of the relationship. The quadratic equation is any equation with the following format:
$$\alpha {\chi }^{2}+b\chi +c=0$$
4
where x signifies an unidentified and a, b and c symbolize known numerical values so that a is not equal to 0. If a = 0, therefore the equation is linear rather than quadratic. a, b and c consist of the quadratic coefficient, linear coefficient, and the constant or free term, respectively. A formal expression for the relationship to be estimated is given as:
The long-run GMM test is conducted to establish stability and inelastic relationships among the instruments used. This test is carried out only on the significant short run coefficients. The long run relationships are introduced and discussed in the study to help proffer proper policy implications to policymakers in relation to the stability of the long-run equilibrium relationship.
The long-run GMM effects for the kth parameter are computed as:
$${B}_{k}÷\left[1-\phi \right]$$
6
The justification for selecting the key variables is discussed as follows: First, fossil fuel combustion and industrial emissions both produce carbon dioxide emissions. Included in them are the emissions of carbon dioxide from gas flaring and the consumption of solid, liquid, and liquid fuels.CO2 emission (metric tons per capita) used as a surrogate for environmental pollution remain a topical issue. Wherever policymakers, the European Union (E.U.), and the United Nations (UN) have taken to the podium, there is little agreement on the results achieved thus far. The inconsistencies suggest that this is an area for further consideration. Finally, banks’ domestic credit to the private sector is employed in this study primarily to provide more awareness about the extent to which financial intermediaries (banks) have contributed to achieving United Nations (UN) sustainable development goals through sustainable finance.
Section 4.1 presents the results on the descriptive statistics for the entire sample. While section 4.2 discusses the two-step system GMM estimates for both linear and quadratic models, section 4.3 presents the results for the long-run estimations. In Sections 4.4 and 4.5, we discuss the study instruments along income level classifications and regional blocks.
4.1. Summary of descriptive statistics and variable correlation
Table III reports the summary statistics, dataset structure, and the correlation matrix. The result did not show any wide deviation for the variables used in the study. Table III further reports the correlation matrix, which analyses the correlation among the variables used in the study. None of the explanatory variables' coefficients exceeded the correlation threshold. In sum, the descriptive statistics and correlation analysis suggest that there is no ample variation in the explanatory variables across the sample. It is therefore convenient to proceed with the rest of the estimations.
[Place Table III here]
4.2. Two-step system GMM estimates for both linear and quadratic models
Model 1 and Model 2, as shown in Table IV, report the SGMM regression results for both linear and quadratic models, respectively, in Eq. (3)–(5). The coefficients are considered as elasticities because they are obtained from log variables. The lag of environmental pollution is highly significant in Model 1 and has a significant explanatory power on current emissions. Thus, at a 1 per cent significance level, the result confirms that countries with prior issues are much more likely to have an adverse effect on carbon emissions. This is demonstrated by the time lapse between past environmental pollution and present environmental pollution. The findings suggest that bank-based development across the globe has positively and significantly contributed to environmental degradation. A 1% growth in bank-based development increases carbon emissions by 0.01%. Based on the results, an increase in bank-based development majorly contributes to environmental pollution. This also suggests that bank-based development hinders environmental quality across the study sample. However, regarding the squared term or turning point of the relationship as shown in Model 2, the study finds a negative coefficient of bank-based development. Thus, at 1% in bank-based development, environmental pollution is reduced by -0.27%. The negative sign of the squared term implies the delinking of bank-based development and CO2 emissions at some threshold. The dataset used, notably, both the linear and nonlinear terms of bank-based development, extends the existence of an inverted U-relationship between bank-based development and environmental degradation. These results further reveal that in the initial stage, an increase in bank-based development activities causes an increase in carbon emissions, which begins to decrease after a certain threshold point.
In addition, the analysis finds that at the 1% and 5% significance levels, an increase in the economic factor, socio-demographic factor (population density), and investment factor (foreign direct investment and net inflows % of GDP) contribute to the reduction of environmental pollution by -0.08%, -0.12%, and − 1.38%, respectively. As shown in Table IV, results for three economic factors, i.e., gross domestic product growth, inflation, exchange rates, and energy-related factors and political factors as captured in Models 1 and 2 are presented and discussed. The result shows that at 1% and 5% significance levels, an increase in economic factors, i.e., gross domestic product growth, inflation, exchange rates, energy-related factors, and political factors, leads to an increase in environmental pollution linearly at 0.21%, 0.58%, 0.24%, 0.03%, 0.01% and non-linearly at 0.16%, 0.56%, 0.32%, 0.05% and 0.01%, respectively. This suggests that a percentage change in these variables, all other things being equal, stimulates environmental pollution.
4.3. Two-step system GMM result for long-run elasticity
Turning to Model 3 as shown in Table IV, the study tests the long-run relationships among the factors that influence environmental pollution. The purpose of testing for a long-term relationship between these variables is to determine the stability of the long-term equilibrium relationship. As demonstrated by Model 3, long-term and at all levels, the findings suggest that bank-based development throughout the world adds positively and significantly to environmental degradation. A 1% improvement in bank-based development increases carbon emissions by 0.64%. Notwithstanding the result for the squared term or turning point of the relationship as shown in Model 3, the study finds a negative coefficient of bank-based development. Thus, at 1% in bank-based development, environmental pollution is reduced by -0.38%. The outcome demonstrates the presence of a long-term U-shaped interaction between bank-based development and environmental degradation. Thus, in the initial stage, an increase in bank-based development activities leads to an increase in carbon emissions, and this starts to decline after a threshold point. The results for the rest of the variables in Model 3 corroborate with the results obtained in Model 1 and 2.
Overall, the long-run coefficients for Model 3 are higher than the coefficients in Model 1, particularly for those variables that are significant at levels. These include bank-based development (0.64% > 0.10%), gross domestic product growth (0.23% > 0.21%), trade openness and investment (-0.079% > -0.078%), energy use (0.78% > 0.60%), population density (-0.15% > -0.12%), government effectiveness (0.45% > 0.24%), inflation (0.06% > 0.03%) and exchange rates (0.01% > 0.00%).
[Place Table IV here]
4.4. Two-step system GMM result for income level estimations
The study segments the datasets into three income classifications. As shown in Table V, while Models 1 and 2 discuss the results for both high income and upper-middle income, Model 3 focuses on lower-middle income. For high-income economies, as shown in Model 1 of Table V, the result shows that at levels, a 1% improvement in bank-based development increases carbon emissions by 0.42 percent. However, the squared term of the relationship finds a negative coefficient of bank-based development. Thus, at 1% in bank-based development, environmental pollution is reduced by -0.25%. The negative sign of the squared term implies the delinking of bank-based development and CO2 emissions at some threshold. This suggests that more support in terms of bank credit to the private sector in high-income countries would contribute immensely to the reduction of carbon emissions. Related results for high-income countries in Model 1 of Table V show that at 1% and 5%, a 1% increase in gross domestic product growth, energy use, government expenditure, and inflation trigger environmental pollution by 0.18%, 0.51%, 0.24%, and 0.17%, respectively. At a 1% significance level, trade openness, investment, and population density lower environmental pollution by -0.0855 and − 0.0806 for high-income countries.
[Place Table V here]
4.5. Two-step system GMM result for regional level estimations
Table VI reports the study results for regional level estimations. Models 1, 2, 3, 4, 5, and 6 describe the outcomes for East Asia and the Pacific, Europe and central Asia, North America, Latin America, and the Caribbean, the Middle East and North Africa, and Sub-Saharan Africa, respectively. Statistically insignificant results are reported, especially for the variables of interest. Nevertheless, it should be noted that for Europe and central Asia North, while energy use, foreign direct investment, and inflation have shown a statistically significant and positive relationship with environmental pollution with a coefficient of 107.39%, 157.52%, and 7.26%, respectively, at 1%, trade openness and investment are negative and significant with a coefficient of 9.60%
[Place Table VI here]
This study chose sixty-two (62) nations based on the availability of data between 1960 and 2020. These nations are further divided into three (3) divisions of income level and six (6) geographical blocks. To lower the instrument count by "collapsing" instruments and the sample size, a two-step system GMM was used for the estimations, along with 3-year averages.
As indicated by the findings of this study, bank-based development has significantly and positively contributed to environmental contamination. Consequently, a growth in bank-based development impairs environmental quality throughout the research sample. The relationship's reversal point, however, reveals a negative coefficient for bank-based development. At some point, when the squared term has a negative sign, it means that bank-based development and CO2 emissions. Thus, an increase in bank-development operations first results in an increase in carbon emissions, which then begins to fall after a threshold point. This results is in line with previous scientific studies Usman et al. (2019) A substantial body of empirical research supports the existence of these results in various cross- and single-country contexts, including Kurniawan and Managi (2018) for Indonesia, Lean and Smyth (2010) for the ASEAN economies, and Ahmed et al. (2017) and Kanjilal and Ghosh (2013).The following are the economic implications due to this result: First, an intensive bank-based development results in an increase in domestic credit to the private sector and consumers, leading to a rise in the consumption of luxury goods such as vehicles and electronic and mechanical gadgets. As a result, various types of emissions Lastly, an increase in bank-based investments may indicate the introduction of multiple new investment projects that utilize fuel for production and transportation. This may also be a means by which bank-based development contributes to an increase in global CO2 emissions. These submissions agree with the results of Zhang (2011) and Raheem et al. (2020) who demonstrated that the expansion of bank assets resulted in a rise in carbon emissions? Surprising, trade liberalization and investment, population density, foreign direct investment, and net inflow all contribute to the decrease in environmental pollution. When an economy is open and there is a steady flow of FDI, it may give more environmentally friendly technologies to the host economy. Hence, an increase in these characteristics could contribute to a reduction in environmental pollution. This result contradicts the findings of Frankel and Lee (1998) and Tamazian and Bhaskara Rao (2010) who argued that bank-based development also draws FDI; the larger FDI inflow in the industrial sector boosts industrial operations, hence increasing environmental pollution. This conclusion has substantial policy implications. Policymakers should look at how too much activity affects the growth of the GDP, inflation, exchange rates, energy issues, and political factors. This is because excessive public spending on development projects could negatively impact environmental quality. Again, to help determine the consistency of the association of long-term equilibrium, the study's long-run coefficients are greater than their short-run counterparts. This shows that the impact of the instruments under examination has both a short-term and a long-term effect on environmental quality and even at a higher magnitude. This result is supported by the findings of Boutabba (2014), who examined the long-term association and direction of causality between carbon emissions and financial development and revealed a positive association between financial development and carbon emissions, suggesting that financial development adds to environmental degradation. Additionally, the results indicate that a rise in bank-based development leads to a rise in carbon emissions in economies with high incomes. The association between bank-based development and environmental quality has a negative coefficient, indicating a concave relationship. Our findings corroborate the claim that bank-based development degrades environmental quality in high-income nations. In Europe and North Central Asia, energy consumption, foreign direct investment, net inflows, and inflation have statistically significant positive correlations with environmental pollution. The demonstrated positive correlation shows that domestic consumer and private sector bank loans contribute to environmental degradation and waste.
The study recommends the following policy implications:
Banking level
First, the results of the study indicate that bank-based development adds positively to environmental impact across all nations, income levels, and geographical classifications. To prevent environmental pollution, the financial authorities of these nations should develop environmentally friendly credit rules that allow bankers to offer green loans to local businesses and consumers. Second, the financial industry, namely the banking sector, should be incentivized to increase their usage of eco-friendly technologies in their daily financial activities. Finally, as the banking industry evolves, consumers and the private sector should have access to a greater number of credits in the field of sustainable financing or green finance. When these credits are kept in check, they can stop people from buying more luxury items like cars and electrical and mechanical gadgets, which cause more pollution.
National level
The authors think that political and social variables influence carbon emissions by introducing a democratic regime and social considerations to the topic of environmental deterioration. According to the study, political stability, corruption, and socio-demographic characteristics have a considerable impact on CO2 emissions. This link indicates that policies and programs that promote a low-carbon lifestyle and energy-efficient policies at the individual level should be advocated at the national level. In addition, the national strategy to examine environmental issues should prioritize the execution of property rights and environmental regulations. To maintain a balance between economic development and carbon emissions, policymakers should also establish green technologies and ecologically friendly energy regulations. Finally, policymakers should evaluate the impact of excessive activity on the gross domestic product, inflation, currency rates, energy-related issues, and political concerns. This result has substantial ramifications for policy. This empirical research offers new avenues for policymakers to construct a comprehensive economic strategy for employing bank-based development as an economic instrument to maintain sustainable levels of environmental quality.
In this study, the kinds or segmentation of carbon emissions in relation to geographical features have not been investigated. Therefore, we consider this to be a significant limitation of our study that should be considered in future research.
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The authors have no competing interest with regards to the manuscript
Availability of Data and Materials
Not available
Authors Contribution
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Isaac Boadi, Ernest Sogah, Solomon Antwi, Freeman Christian Gborse, and Andrews Ayiku. The first draft of the manuscript was written by Isaac Boadi, Ernest Sogah and John Kwaku Mensah Mawutor and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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- Rashid Khan HU, Islam T, Yousaf SU, Zaman K, Shoukry AM, Sharkawy MA, Gani S, Aamir A, Hishan SS (2019) The impact of financial development indicators on natural resource markets: Evidence from two-step GMM estimator. Resour Policy 62. https://doi.org/10.1016/j.resourpol.2019.04.002
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Table I: Summary of strands of literature |
|
|
|
|||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Economic activities and environmental degradation |
Do macroeconomic uncertainty and financial developmennt cause environmental degradation? Evidence from an emerging economy. |
Examine the impact of macroeconomic unpredictability on environmental degradation in India between 1971 and 2016. |
Autoregressive distributive lag bound testing model and Toda–Yamamoto causality approach |
India |
Carbon emissions are positively impacted by macroeconomic uncertainty, research shows. Environmental quality has been negatively influenced by the growth of the financial sector, the economy, and energy use. |
Positive |
International Journal of Social |
Bijoy, R. and Neog, Y. (2021) |
|
The role of education in the Environmental Kuznets Curve. Evidence from Australian data |
The fundamental tenet of this stidy is that a society's usage of energy resources is substantially influenced by its level of education. |
n.a |
Australia |
Most of the analyzed period, according to empirical findings, saw an increase in the education rate that more than offset an increase in per capita CO2 emissions brought on by economic growth. |
Positive |
Energy Economics |
Balaguer, J. and Cantavella, M. (2018), |
|
|
|
What drives environmental degradation? Evidence from 14 Sub-Saharan African countries |
Consider the major contributions made by economic expansion, the use of renewable and non-renewable energy sources, and urbanization when examining the factors that contribute to environmental deterioration. |
The Augmented Mean Group (AMG) estimator |
14 Sub-Saharan Africa (SSA) countries |
The Ecological Frontier (EF) in the SSA nations benefits from economic expansion, non-renewable energy use, and urbanization. |
Positive |
The Science of the Total Environment |
Wang, J. and Dong, K. (2019) |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
|
|
|
|||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Economic activities and environmental degradation |
Economic growth, CO2 emissions and energy consumption: What causes what and where? |
Investigates the dynamic causal link between economic expansion, carbon emissions, and energy use |
Panel vector autoregression (PVAR) along with a system-generalized method of moment (System-GMM). |
116 countries |
Economic expansion does not improve the energy efficiency on a regional and global scale. Economic boom, however, has a detrimental effect on carbon emissions in the Caribbean and Latin America as well as globally. |
Positive/Negative |
Energy Economics |
Acheampong, A.O. (2018) |
|
The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. |
This study examines the impact of sources of electricity produced using renewable energy on CO2 emissions in 23 different European nations. |
Pedroni cointegration, VECM Granger causality and fully modified ordinary least-square. |
23 selected European countries |
The findings showed that trade liberalization decreases Emissions of carbon dioxide whereas GDP growth, industrialization, and economic deepening raise them over time. |
Negative |
Natural Hazards |
Al-Mulali, U., Ozturk, I. and Lean, H.H. (2015) |
|
|
|
Environmental degradation and economic growth: evidence for a developing country. |
From 1971 to 2010, the research investigates the connection between economic development and environmental degradation in Ecuador. |
Johansen co-integration tests aad ECM. |
Ecuador |
The findings reveal an adverse link with both real GDP and natural vegetation, demonstrating that such a nation's economic output is reliant on environment degradation. |
Positive |
Environment, Development and Sustainability |
Alvarado, R. and Toledo, E. (2017) |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
|
|
|
|||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Economic activities and environmental degradation |
Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. |
The purpose of the article would be to investigate the causal link between Tunisia's urbanization, trade openness, financial development, and real GDP (gross domestic product). |
Auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). |
Tunisia. |
According to the Tunisian scenario, real GDP and Emissions of carbon dioxide have quite a strong monotonic connection. |
Positiv |
Environmental Science and Pollution Research |
Farhani, S. and Ozturk, I. (2015) |
|
Financial development and environmental degradation: do political regime matter? |
The research looks at the factors that contributed to environmental degradation in 26 African nations between 1985 and 2011. |
A wide range of econometric techniques (Chow test, cross-country regressions and the Generalized Method of Moments (GMM)). |
26 African countries |
Economic expansion and urbanization are important predictors of environmental degradation, according to cross-country regression analyses. |
Negative |
Journal of Cleaner Production |
Adams, S. and Klobodu, E.K.M. (2018) |
|
|
|
Effect of economic growth on CO2 emission in developing countries: evidence from a dynamic panel threshold model. |
Using an instrumental variable thresholds approach, this research evaluated how economic growth affects CO2 emissions. |
Panel causality methods |
31 developing countries. |
According to the findings, economic expansion has a marginally positive influence on CO2 emissions in high growth regimes but a marginally negative effect in low growth regimes. |
Negative/Positive |
Cogent Economics and Finance |
Aye, G.C. and Edoja, P.E. (2017). |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
|
|
|
|||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Energy consuption and carbon emmission |
CO2 emissions, energy consumption and economic growth in Turkey. |
The research looks at the concerns of long-term and causative relationships between economic growth, carbon emissions, energy consumption, and employment ratio. |
Autoregressive distributed lag bounds testing approach of cointegration |
Turkey |
According to the overall findings, Turkey's real production growth will probably not be adversely affected by energy conservation measures like restricting energy use and reducing carbon dioxide emissions. |
Positive |
Renewable and Sustainable Energy Reviews |
Ozturk, I. and Acaravci, A. (2010) |
|
Effect of economic growth on CO2 emission in developing countries: evidence from a dynamic panel threshold model. |
The dynamic panel threshold approach was used in this study to examine how economic expansion affects CO2 emissions. |
Panel causality methods |
31 developing countries. |
It was discovered that population and energy use had a positive and considerable impact on CO2 emissions. |
Positive |
Cogent Economics and Finance |
Aye, G.C. and Edoja, P.E. (2017). |
|
|
Economic growth, CO2 emissions and energy consumption: What causes what and where? |
The dynamic panel threshold approach was used in this study to examine how economic expansion affects CO2 emissions. |
Panel vector autoregression (PVAR) along with a system-generalized method of moment (System-GMM). |
116 countries |
Energy use has a favorable impact on carbon emissions in the Middle East and North Africa (MENA) but has a negative impact on emissions in sub-Saharan Africa and the Caribbean and Latin America. |
Positive/Negative |
Energy Economics |
Acheampong, A.O. (2018) |
|
|
|
The energy consumption and emission of |
Examine whether polyurethane (PU) pavement satisfies the demands of energy. |
Life cycle environmental impact assessment (LCIA) |
Various countries |
In terms of energy use over the course of a pavement's life, asphalt is 1.79 times more energy-intensive than PU pavement. It suggests that using PU pavement can result in energy savings. |
Positive |
Journal of Cleaner Production |
Cong, L., Guo, G., Yu, M., Yang, F. and Tan, L. (2020), “ |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
|
|
||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
||
|
Energy consuption and carbon emmission |
What drives environmental degradation? Evidence from 14 Sub-Saharan African countries |
Examine the factors that affect environmental deterioration while considering the major contributions made by economic expansion, the use of renewable and non-renewable energy sources, and urbanization |
The Augmented Mean Group (AMG) estimator |
14 Sub-Saharan Africa (SSA) countries |
The Econological Frontier is adversely affected by the utilization of renewable energy. Furthermore, urbanization, the EF, non-renewable demand, and economic expansion all share a bidirectional long-run causal link. |
Negaitive/ Bdirectional |
The Science of the Total Environment |
Wang, J. and Dong, K. (2019) |
||
|
|
The influence of economic growth, urbanization, trade openness, financial development, and renewable energy on pollution in Europe. |
In this study, the effects of producing electricity from renewable sources on CO2 emissions are examined for 23 different European nations between 1990 and 2013. |
Pedroni cointegration, VECM Granger causality and fully modified ordinary least-square. |
23 selected European countries |
In contrast to renewable power, which have negligible long-term effects on Carbon dioxide emission, combustion renewable and waste, hydro power, as well as nuclear power all produce renewable electricity. |
Negative/ Insignificant |
Natural Hazards |
Al-Mulali, U., Ozturk, I. and Lean, H.H. (2015) |
||
|
Source: Author's compilation (2022) |
||||||||||
|
Table I: Summary of strands of literature |
|
|||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Energy consuption and carbon emmission |
Relationships among carbon |
The study uses annual time series data for India, Indonesia, China, and Brazil from 1970 to 2012 to analyze the effects of wealth, energy use, and population growth on CO2 emissions. |
Autoregressive Distributed Lag (ARDL) bounds test approach. |
Brazil, China, India, and Indonesia. |
This essay's goal is to examine how the Indian nation's economic financial deepening, economic expansion, and energy use affect the environment. |
Negative |
Ecological Indicators |
Alam, M.M., Murad, M.W., Noman, A.H.M. and Ozturk, I. (2016) |
|
|
The impact of financial development, economic growth and energy consumption on development, economic growth and energy consumption on environmental degradation and energy consumption on environmental degradation: Evidence from India |
This essay's goal is to examine how the Indian nation's economic financial deepening, economic expansion, and energy utilization impact the climate. |
Autoregressive Distributed Lag bounds testing approach, error correction method (ECM) and VECM. |
India |
Economic expansion, power usage, and accounting information are the main source of deaths deterioration. |
Negative |
Management of Environmental Quality |
Najeb Masoud, Glenn Hardaker, (2012). |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
|||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
|
Financial development and environmental degradation |
Financial development and energy demand in the United States: new evidence from combined cointegration and asymmetric causality tests. |
Examine the integrating qualities and determine whether there is a long-term correlation between the following variables: capital, trade, FDI, wealth creation, financial development, and energy consumption. |
LM unit root test based on residual augmented least squares (RALS) regression under structural break, Bayer–Hanck cointegration approach and asymmetric causality test. |
United States |
While commerce and capital have a long-term positive impact on energy consumption, financial development, FDI, and real GDP increase energy demand. |
Positive/ Negatives |
Energy |
Farhani, S. and Solarin, S. (2017). |
|
|
Does higher economic and financial |
The study looks on the relationship between financial development as well as environmental quality and economic development. |
Using standard reduced-form modeling approach |
BRIC economies. |
Environmental deterioration is reduced when economic and financial development increases. Therefore, openness and financial liberalization are crucial for reducing CO2. |
Positive |
Energy Policy |
Tamazian, A., Chousa, J.P. and Vadlamannati, K.C. (2009) |
||
|
Does financial instability increase environmental degradation? Fresh evidence from Pakistan. |
Examines the connection between environmental destruction and financial instability using a multivariate methodology. |
ARDL bounds testing approach to cointegration, and error correction method (ECM). |
Pakistan |
Financial instability increases environmental degradation. |
Positive |
Economic Modelling |
Shahbaz, M. (2013) |
||
|
|
Do economic, financial and institutional developments matter for |
By examining the connections between financial development and institutional quality as well as between financial development and environmental quality, this research seeks to close this gap in the literature. |
Standard reduced-form modelling approach and GMM estimation to control for endogeneity. |
24 transition economies |
Financial liberalization could have a negative impact on environmental quality if it is not implemented within a solid institutional framework. |
Negative |
Energy Economics |
Tamazian, A. and Rao, B.B. (2010) |
|
|
Source: Author's compilation (2022) |
|||||||||
|
Table I: Summary of strands of literature (Con't) |
|
||||||||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|||
|
Financial development and environmental degradation |
Environmental degradation in France: the effects of FDI, financial development, and energy innovations. |
To fully understand how FDI, financial development, economic growth, energy consumption, and energy research discoveries affect CO2 emissions in France, the article takes them all into account. |
Bootstrapping bounds testing approach |
France |
Financial growth reduces carbon emissions, enhancing the quality of the French environment. |
Positive |
Energy Economics |
Shahbaz, M., Nasir, M.A. and Roubaud, D. (2018). |
|||
|
Financial development and environmental degradation: does political regime matter? |
In this study, the causes of environmental deterioration in 26 African nations between 1985 and 2011 are examined. |
A wide range of econometric techniques (Chow test, cross-country regressions and the Generalized Method of Moments (GMM)). |
26 African countries |
After adjusting for political regime, financial development becomes a substantial factor in environmental pollution. |
Negative |
Journal of Cleaner Production |
Adams, S. and Klobodu, E.K.M. (2018) |
||||
|
The impact of financial development indicators on natural resource markets: evidence from two-step GMM estimator. |
In this study, the causes of environmental deterioration in 26 African nations between 1985 and 2011 are examined. |
Two-step GMM estimator. |
China |
With the few exceptions, domestic financial sector credit has a negative impact on the markets for resources and energy. |
Negative |
Resources Policy |
Khan, H.U.R., Islam, T., Yousaf, S.U., Zaman, K., Shoukry, A.M., Sharkawy, M.A., Gani, S., Aamir, A. |
||||
|
|
O2 emissions and financial development in an emerging economy: an |
The study investigates how a tiny emerging nation's economic carbon pollution is impacted by financial and economic growth. |
Augmented VAR approach, Error Correction Model (ECM) and Vector Error Correction Model (VECM) |
n.a |
Only during the latter period, when there was a greater degree of liberalization and financial sector development, did the financial factors play a role in emission mitigation. |
Positive |
Energy Policy |
Abbasi, F. and Riaz, K. (2016) |
|||
|
Source: Author's compilation (2022) |
|||||||||||
|
Table I: Summary of strands of literature (Con't) |
||||||||||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
|||||||
|
Financial development and environmental degradation |
Does the Kuznets curve apply for financial development and environmental |
Establishes the relationship between financial growth and environmental damage. |
Autoregressive distributed lag, including the pooled mean group model; the mean group; the dynamic fixed effect estimator and second-generation Granger test. |
Asia-Pacific region |
It is discovered that financial progress and environmental destruction are entangled in an Inverted relationship. |
Negative/Concave |
Heliyon |
Duc Hong Vo , Nhan Thien Nguyen, Anh The Vo, Chi Minh Ho, Thang Cong Nguyen (2021) |
||||||||
|
Time-Varying Impact of Financial Development on Carbon Emissions in G-7 Countries: Evidence from the Long History |
The article examines the changing effects of financial growth on carbon dioxide (CO2) emissions for the G-7 countries across a significant historical span. |
Bootstrap time-varying co-integration (TVC), bootstrap rolling window estimation approaches and polynomial trends. |
Canada, France, Germany, Italy, Japan, the United Kingdom (UK), and the United State (US) |
In Canada, Japan, and the US, the financial deepening on CO2 emissions has historically had a M form. In France, Italy, and the UK, it has taken an inverted N shape. In Germany, it has taken an inverted M form (W form). |
M-shaped & N-shaped |
Munich Personal RePEc Archive |
Shahbaz, Muhammad and Destek, Mehmet Akif and Dong, Kangyin and Jiao, Zhilun (2021) |
|||||||||
|
|
The impact of financial development, economic growth and energy consumption on development, economic growth and energy consumption on environmental degradation and energy consumption on environmental degradation: Evidence from India |
This article's objective is to examine how the Indian economy's environmental damage is impacted by financial development, economic boom, and energy use. |
Autoregressive Distributed Lag bounds testing approach, error correction method (ECM) and VECM. |
India |
In India, environmental damage seems to be becoming worse as the economy develops. |
Negative |
|
|
||||||||
|
Source: Author's compilation (2022) |
||||||||||||||||
|
Table I: Summary of strands of literature (Con't) |
||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Investment factors and environmental degradation |
The Impacts of FDI Inflows on Carbon Emissions: Economic Development and Regulatory Quality as Moderators |
The goal of this research is to examine the effects of FDI inflows on carbon emissions and further evaluate the influencing mechanisms through the mitigating consequences of financial development and proper quality. |
feasible generalized least squares (FGLS). |
G20 economies |
Inflow of FDIs have a favorable relationship with carbon emissions. Despite FDI inflows frequently lead to increase in carbon dioxide emissions, they are much more likely to reduce carbon pollution in nations with advanced economies and high-quality regulations. |
Positive & Nagative |
Frontier. Energy Research |
Huang, Y. et al. (2022) |
|
The impact of financial development indicators on natural resource markets: evidence from two-step GMM estimator. |
Analyses of funding initiatives to assist the marketplaces for environmental assets from 1967 to 2016. |
Two-step GMM estimator. |
China |
FDI inflows largely influenced soft and hard commodity markets to decrease natural resource rents and agricultural & livestock productions, except total fisheries production, which substantially increases FDI inflows in a country. |
Negative |
Resources Policy |
Khan, H.U.R., Islam, T., Yousaf, S.U., Zaman, K., Shoukry, A.M., Sharkawy, M.A., Gani, S., Aamir, A. |
|
|
|
Environmental degradation in France: the effects of FDI, financial development, and energy innovations. |
To fully understand the factors that affect Greenhouse gasses within France, the article takes into account the crucial roles that FDI, financial deepening, wealth creation, energy usage, and fuel research discoveries play. |
Bootstrapping bounds testing approach |
France |
Since FDI harms the ecosystem, it is believed that France is a pollution refuge. |
Negative |
Energy Economics |
Shahbaz, M., Nasir, M.A. and Roubaud, D. (2018). |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Investment factors and environmental degradation |
Do macroeconomic uncertainty and financial development cause environmental degradation? Evidence from an emerging economy. |
Investigate the effect of macroeconomic |
Autoregressive distributive lag bound testing model and Toda–Yamamoto causality approach |
India |
Due to the fact that FDI inflow occasionally provides green, effective, environmentally friendly processes and better green innovation, FDI inflow favorably improves the quality of the environment of a host economy. |
Positive |
International Journal of Social |
Bijoy, R. and Neog, Y. (2021) |
|
Does trade cause growth? |
The paper investigates the impact of trade on environmental pollution. |
Ordinary least-squares |
Sub-Saharan Africa, Qatar, Trinidad and Tobago, Hong Kong,SyriaLuxembourg and Singapore |
The growth in FDI influx into the industrial sector, which increases industrial activity and generates air degradation, is a result of financial expansion. |
Negative |
The American Economic Review |
Frankel, J.A. and Romer, D. (1999) |
|
|
|
Financial development and energy demand in the United States: new evidence from combined cointegration and asymmetric causality tests. |
Explore the integrating properties and to check whether a long run relationship exists among energy demand, financial development, economic growth, foreign direct investment (hereafter FDI), trade and capital. |
LM unit root test based on residual augmented least squares (RALS) regression under structural break, Bayer–Hanck cointegration approach and asymmetric causality test. |
United States |
Financial development also draws FDI; the additional industry brought on by the FDI causes pollution. |
Positive/Negatives |
Energy |
Farhani, S. and Solarin, S. (2017). |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
||||||||
|
Strands of literature |
Title |
Purpose/Main reseach question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Socio-demographic factors and environmental degradation |
Relationships among carbon |
Using time series data for the countries of India, Indonesia, China, and Brazil from 1970 to 2012, the research evaluates the effects of wealth, energy use, and population increase on CO2 emissions. |
Autoregressive Distributed Lag (ARDL) bounds test approach. |
Brazil, China, India, and Indonesia. |
While the relationship between CO2 emissions and population growth was found to be statistically significant for India and Brazil, it has been statistically insignificant for China and Indonesia in both the short run and long run. |
Positive |
Ecological Indicators |
Alam, M.M., Murad, M.W., Noman, A.H.M. and Ozturk, I. (2016) |
|
|
What drives carbon dioxide emissions in the long run? Evidence from selected South Asian Countries |
Energy use, CO2 emissions, income, trade openness, and population |
Pedroni- Kao- and |
Five South Asian |
In the long run, energy consumption, trade openness and population have negative impact on CO2 and |
Negative |
Renewable and |
Ahmed et al. (2017) |
|
Source: Author's compilation (2022) |
||||||||
|
Table I: Summary of strands of literature (Con't) |
||||||||
|
Strands of literature |
Title |
Purpose/Main research question |
Methodology |
Countries |
Findings |
Sign of impact |
Journal |
Source |
|
Political factors and environmental degradation |
Political influences on greenhouse gas emissions from US states |
The study analyzes variations in CO2 emissions over time and between US states to determine the extent to which politics mitigates the effects of the economic and demographic factors on social and environmental effects. |
Hierarchical models |
United States |
Long considered to be two of the main causes of environmental stress, population, and wealth. A vehicle for evaluating the impact of many postulated human main causes of climate transformation is provided by our methodologies and the possibility for government to mitigate the consequences of the magnitude of industrial operations on the environment. Negative |
Negative |
PNAS |
Thomas Dietz, Kenneth A. Frank, Cameron T. Whitley, Jennifer Kelly, and Rachel Kelly (2015) |
|
|
Do political and social factors affect carbon emissions? Evidence from international data. |
By investigating how sociopolitical factors affect pollution levels, the study adds to the body of knowledge about factors that affect carbon dioxide emissions. |
Autoregressive distributed lag approach |
145 countries worldwide |
Environmental costs are greatly influenced by political instability, corruption, and the presence of women in politics. |
Positive |
Applied Economics |
Benlemlih, M, Assaf, C El Ouadghiri, I (2022). |
|
Source: Author's compilation (2022) |
||||||||
|
Table II: Variable definitions |
||||
|
Variable |
Proxies |
Symbols |
Expected Hypothesis |
Source |
|
Dependent variable |
||||
|
Co2 emmission (Metric tons per capita) |
Environmental factor |
Co2_EM |
? |
Database of World Bank Development Indicators |
|
Independent variable |
Database of World Bank Development Indicators |
|||
|
Domestic credit to the private sector % GDP |
Bank-based development |
DC_PS |
+ |
Database of World Bank Development Indicators |
|
Gross domestic product growth (Annual %) |
Economic factors |
GDP_G |
+ |
Database of World Bank Development Indicators |
|
Trade openness and investment |
Economic factors |
TOP_I |
+ |
Database of World Bank Development Indicators |
|
Inflation, consumer prices (annual %) |
Economic factors |
IFL_P |
-/+ |
Database of World Bank Development Indicators |
|
Exchange rate |
Economic factors |
EXC_R |
-/+ |
Database of World Bank Development Indicators |
|
Energy use (kg of oil equivalent per capita) |
Energy-related factor |
ENE_U |
-/+ |
Database of World Bank Development Indicators |
|
Foreign direct investment and net inflows (% OF GDP) |
Investment factor |
FDI_NI |
+ |
Database of World Bank Development Indicators |
|
Population density (people per sq. km of land area) |
Socio-demographic factors |
POP_D |
- |
Database of World Bank Development Indicators |
|
Government effectiveness |
Political factor |
GOV_E |
-/+ |
Database of World Bank Development Indicators |
|
Source: Author's compilation (2022) |
||||
|
Table III: Description statistics and Pearson Correlation |
|||||||||||
|
Variables |
Co2_EM |
DC_PS |
DC_PS^2 |
GDP_G |
TOP_I |
ENE_U |
FDI_NI |
POP_D |
GOV_E |
IFL_P |
EXC_R |
|
Description statistics |
|||||||||||
|
Mean |
1.1610 |
3.1200 |
6.6040 |
4.2270 |
4.0590 |
7.1400 |
7.6030 |
4.0070 |
1.0180 |
3.6800 |
2.1190 |
|
Standard deviation (SD) |
0.9320 |
1.8610 |
3.1810 |
0.1050 |
1.0430 |
1.0850 |
0.0180 |
1.4610 |
0.3580 |
0.4000 |
2.1140 |
|
Minimum |
-0.0200 |
0.0000 |
1.3860 |
-0.0480 |
0.0000 |
2.2560 |
6.5960 |
0.4900 |
-0.6610 |
-0.2790 |
0.0000 |
|
Maximum |
4.4850 |
5.4040 |
10.8170 |
5.3710 |
6.0830 |
10.0040 |
8.0960 |
9.8630 |
1.6930 |
10.0780 |
9.5880 |
|
Observation |
9,010 |
1,829 |
1,829 |
8,650 |
1,829 |
5,769 |
7,667 |
9,449 |
3,974 |
7,502 |
1,829 |
|
Expected sign |
? |
+ |
- |
+ |
+ |
-/+ |
+ |
- |
-/+ |
-/+ |
-/+ |
|
Pearson Correlation |
|||||||||||
|
Co2_EM |
1 |
||||||||||
|
DC_PS |
0.0840* |
1 |
|||||||||
|
DC_PS^2 |
0.1030* |
0.9990* |
1 |
||||||||
|
GDP_G |
-0.0300* |
-0.0350 |
-0.0420 |
1 |
|||||||
|
TOP_I |
0.1420* |
0.1680* |
0.1690* |
-0.0040 |
1 |
||||||
|
ENE_U |
0.9400* |
0.1070* |
0.1280* |
-0.0500* |
0.1650* |
1 |
|||||
|
FDI_NI |
0.0270* |
0.0930* |
0.0950* |
0.0230 |
0.2150* |
0.0700* |
1 |
||||
|
POP_D |
0.1040* |
0.1660* |
0.1700* |
0.0050 |
0.2870* |
-0.0230 |
0.0550* |
1 |
|||
|
GOV_E |
0.6290* |
0.3180* |
0.3520* |
-0.0510* |
0.2520* |
0.6480* |
0.0760* |
0.1820* |
1 |
||
|
IFL_P |
-0.1010* |
-0.2510* |
-0.2580* |
-0.1480* |
-0.2560* |
-0.1530* |
-0.0770* |
-0.0990* |
-0.3300* |
1 |
|
|
EXC_R |
-0.3330* |
-0.1850* |
-0.1930* |
0.1300* |
-0.1450* |
-0.349* |
-0.1030* |
0.0810* |
-0.3300* |
-0.0020 |
1 |
|
*** p<0.01, ** p<0.05, * p<0.1 |
|||||||||||
|
Source: Author's computations (2022) |
|||||||||||
|
Table IV: Two-step system GMM estimates for short-run, quadratic model and long-run estimations |
||||
|
Variables |
Model (1) |
Model (2) |
Model (3) |
|
|
|
linear Model |
Quadratic model |
Long-run |
|
|
Lag_Co2_EM |
0.2753*** |
0.2792*** |
- |
|
|
(0.0129) |
(0.0168) |
- |
||
|
DC_PS |
0.0047*** |
0.4639*** |
0.6440*** |
|
|
(0.0016) |
(0.0562) |
(0.0800) |
||
|
DC_PS^2 |
- |
-0.2733*** |
-0.3790*** |
|
|
- |
(0.0328) |
(0.0470) |
||
|
GDP_G |
0.2143*** |
0.1648*** |
0.2290*** |
|
|
(0.0118) |
(0.0190) |
(0.0300) |
||
|
TOP_I |
-0.0786*** |
-0.0546*** |
-0.0790*** |
|
|
(0.0133) |
(0.0128) |
(0.0180) |
||
|
ENE_U |
0.5892*** |
0.5620*** |
0.7800*** |
|
|
(0.0152) |
(0.0159) |
(0.0120) |
||
|
FDI_NI |
-1.3830** |
-0.4756 |
- |
|
|
(0.6522) |
(0.7380) |
- |
||
|
POP_D |
-0.1205*** |
-0.1095*** |
-0.1520*** |
|
|
(0.0056) |
(0.0093) |
(0.0120) |
||
|
GOV_E |
0.2395*** |
0.3249*** |
0.4510*** |
|
|
(0.0259) |
(0.0408) |
(0.0550) |
||
|
IFL_P |
0.0275*** |
0.0453*** |
0.0630*** |
|
|
(0.0077) |
(0.0098) |
(0.0140) |
||
|
EXC_R |
0.0067** |
0.0083** |
0.0115** |
|
|
(0.0028) |
(0.0031) |
(0.0042) |
||
|
Observations |
841 |
841 |
- |
|
|
Number of code |
56 |
56 |
- |
|
|
Time dummies |
Yes |
Yes |
- |
|
|
Sargan p-value |
0 |
0 |
- |
|
|
Hansen p-value |
0.999 |
0.999 |
- |
|
|
Standard errors in parentheses |
||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
||||
|
Source: Author's computations (2022) |
||||
|
Table V: Two-step system GMM estimates for income level classifications |
|||
|
Variables |
Model (1) |
Model (2) |
Model (3) |
|
|
High Income |
Upper-Middle income |
Lower-Middle income |
|
Lag_Co2_EM |
0.3382*** |
-0.0979 |
0.0000 |
|
(0.0296) |
(0.2835) |
(0.0000) |
|
|
DC_PS |
0.4233*** |
0.3983 |
0.0000 |
|
(0.0747) |
(0.7410) |
(0.0000) |
|
|
DC_PS^2 |
-0.2460*** |
-0.2357 |
0.0136 |
|
(0.0448) |
(0.4289) |
(0.0294) |
|
|
GDP_G |
0.1808*** |
-0.2464 |
-1.2328 |
|
(0.0441) |
(0.2500) |
(1.0304) |
|
|
TOP_I |
-0.0855*** |
0.0250 |
0.1123 |
|
(0.0218) |
(0.3013) |
(0.2082) |
|
|
ENE_U |
0.5072*** |
1.3503*** |
0.0000 |
|
(0.0353) |
(0.3783) |
(0.0000) |
|
|
FDI_NI |
-0.2154 |
-28.7730 |
0.0000 |
|
(0.3620) |
(29.3115) |
(0.0000) |
|
|
POP_D |
-0.0806*** |
-0.8110 |
0.4696 |
|
(0.0137) |
(0.9085) |
(0.2945) |
|
|
GOV_E |
0.2431** |
-0.2265 |
0.6016 |
|
(0.1115) |
(0.5352) |
(0.9257) |
|
|
IFL_P |
0.1692*** |
0.0528 |
0.0000 |
|
(0.0221) |
(0.2624) |
(0.0000) |
|
|
EXC_R |
0.0070 |
0.0434 |
0.0012 |
|
(0.0047) |
(0.1317) |
(0.0163) |
|
|
Observations |
551 |
201 |
89 |
|
Number of code |
36 |
14 |
6 |
|
Time dummies |
Yes |
Yes |
Yes |
|
Sargan p-value |
0 |
0.00281 |
0.185 |
|
Hansen p-value |
1 |
1 |
1 |
|
Standard errors in parentheses |
|||
|
*** p<0.01, ** p<0.05, * p<0.1 |
|||
|
Source: Author's computations (2022) |
|||
|
Table VI: Two-step system GMM estimates for regional classifications |
||||||
|
|
Model (1) |
Model (2) |
Model (3) |
Model (4) |
Model (5) |
Model (6) |
|
Variables |
East Asia and pacific |
Europe and central Asia North |
North America Latin |
Latin America and Caribbean |
Middle East and North Africa |
Sub-Saharan Africa |
|
|
|
|
|
|
|
|
|
Lag_Co2_EM |
3.1417 |
0.0737** |
0.0000 |
0.2717 |
-0.4114 |
0.0000 |
|
(7.3370) |
(0.0291) |
(0.0000) |
(0.6681) |
(0.5805) |
(0.0000) |
|
|
DC_PS |
3.2672 |
0.2096 |
0.0000 |
0.0000 |
0.0000 |
0.0000 |
|
(4.9233) |
(0.1535) |
(0.0000) |
(0.0000) |
(0.0000) |
(0.0000) |
|
|
DC_PS^2 |
-1.9911 |
-0.1275 |
0.0169 |
-0.0192 |
-0.2602 |
0.1344 |
|
(3.0160) |
(0.0914) |
(0.0138) |
(0.0636) |
(0.2277) |
(0.0823) |
|
|
GDP_G |
-0.2426 |
0.0532 |
0.0000 |
-0.1419 |
0.0000 |
0.0000 |
|
(2.6328) |
(0.0683) |
(0.0000) |
(0.3264) |
(0.0000) |
(0.0000) |
|
|
TOP_I |
0.4038 |
-0.0960** |
-0.7481 |
-0.1601 |
0.0000 |
0.3288 |
|
(0.3735) |
(0.0389) |
(0.3567) |
(0.5131) |
(0.0000) |
(0.2715) |
|
|
ENE_U |
-0.1805 |
1.0739*** |
0.0000 |
0.8685 |
0.9731* |
0.0000 |
|
(4.7773) |
(0.0438) |
(0.0000) |
(0.5013) |
(0.3994) |
(0.0000) |
|
|
FDI_NI |
0.0000 |
1.5752** |
0.0000 |
0.0000 |
0.0000 |
0.0000 |
|
(0.0000) |
(0.6636) |
(0.0000) |
(0.0000) |
(0.0000) |
(0.0000) |
|
|
POP_D |
-1.4632 |
-0.2310 |
0.0000 |
0.5895 |
0.2415 |
0.0000 |
|
(15.5624) |
(0.1757) |
(0.0000) |
(2.3118) |
(0.4788) |
(0.0000) |
|
|
GOV_E |
-0.2022 |
0.1209 |
0.0000 |
-0.0077 |
0.0000 |
0.0000 |
|
(4.8000) |
(0.0990) |
(0.0000) |
(1.4060) |
(0.0000) |
(0.0000) |
|
|
IFL_P |
-2.1094 |
0.0726** |
0.0000 |
0.2752 |
-0.4967 |
0.0000 |
|
(5.3742) |
(0.0265) |
(0.0000) |
(0.6100) |
(0.6641) |
(0.0000) |
|
|
EXC_R |
-0.3476 |
-0.0003 |
0.0000 |
0.0268 |
0.0000 |
0.0000 |
|
(0.4436) |
(0.0061) |
(0.0000) |
(0.0507) |
(0.0000) |
(0.0000) |
|
|
Observations |
154 |
406 |
32 |
125 |
73 |
30 |
|
Number of ccode |
10 |
26 |
2 |
9 |
5 |
2 |
|
Time dummies |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
|
Sargan p-value |
0.00484 |
0.0969 |
0.0586 |
0.237 |
0.160 |
0.0518 |
|
Hansen p-value |
1 |
1 |
1 |
1 |
1 |
1 |
|
Standard errors in parentheses |
||||||
|
*** p<0.01, ** p<0.05, * p<0.1 |
||||||
|
Source: Author's computations (2022) |
||||||