Income inequality, ecological footprint, and carbon dioxide emissions in Asian developing economies: what effects what and how?

The reduction of income inequality and environmental vulnerability is the most important factor, through which we can achieve the target of Sustainable Development Goals (SDGs). The past papers have investigated the nexus between income inequality and carbon emissions; however, the relationship between income inequality and carbon emissions along with ecological footprint has not been studied in the case of developing countries. To this end, this study analyzed the impact of income inequality on both carbon emissions and ecological footprint as well as the impact of carbon emission and ecological footprint on income inequality by using the dataset from 2006 to 2017 for the 18 Asian developing economies. This study confirmed the positive relationship between carbon emissions, ecological footprint, and income inequality under the methodology of Driscoll and Kraay (D&K) standard error approach. Specifically, a higher-income gap is destructive for environmental degradation, whereas increasing level of carbon emissions and ecological footprint also leads to rising income inequality in the investigated region. Furthermore, foreign direct investment (FDI), easy access to electricity, and population growth control income inequality, but they have a detrimental effect on both ecological footprint and carbon emissions. The empirical findings also provide some important policy implications.


Introduction
Is that possible for developing economies to meet environmental goals and also ensure low income inequality? Does economic growth break the vicious circle of poverty, mainly, if it is not linked with increasing environmental degradation and income inequality? These questions are theoretically and empirically ambiguous as rising environmental degradation income inequality and poverty are major challenges facing every human being in the twenty-first century. Despite a significant alleviation in the poverty level from the last few years, many developing countries are still facing the problem of increasing income inequality (Baloch et al. 2020a). In contrast, the level of toxic emissions (Carbon emissions, ecological footprint, etc.) is reaching its limits and crossing dangerous zone (Steffen et al. 2015). There are large numbers of research studies that investigate the association between inequality and toxic emissions; the greenhouse or conservatory effect is mainly responsible for the rising global warming and climate change. Many research studies in the literature propose that economic growth, however, up to a certain level of economic development, upsurges global warming and greenhouse gases emissions (Forabosco et al. 2017;Edenhofer et al. 2014;Tyler et al. 2017). Subsequently, in the context of least developed countries, higher economic growth may ensure poverty reduction but increase environmental degradation. An interrelated problem is whether the trade-off situation exists between income inequality and environmental degradation, as suggested by the work of Ravallion et al. (2000). As the readers will see below, the empirical and theoretical studies produce miscellaneous findings on this issue, pointing to different mechanisms and effects (for a survey, see Berthe and Elie 2015). Most of the past research papers are, however, based on rather simple analytical techniques and old data; therefore, in the current study, we improve the existing literature in both of these prospects, that is, apply advanced econometric techniques and incorporated expanded data.
In the meantime, the increasing income gap between rich and poor creates miscellaneous consequences on different social classes of the society (Liu et al. 2020). The poor people of the society get more affected by the income inequality since they are an extremely vulnerable part of the society. In addition, rising income inequality is degrading the environment by generating problems in the way of policy implications. Likewise, income inequality might lead to less environmental protection and eventually be responsible for environmental degradation. Furthermore, it is argued that a higher income gap increases environmental degradation through voting and ownership channels (Hailemariam et al. 2020). Meanwhile, it is further documented that better environmental quality could be significant for the reduction of absolute poverty (Dorn et al. 2021;Xi 2020). Natural (environmental) resources help to reduce alleviate poverty and inequality by providing equal and better health facilities, equal access to food, and job opportunities (Lawson et al. 2012). Alternatively, environmental pollution can be controlled by controlling the increasing income gap, mainly in less developed countries. It is argued that environmental degradation and rising income inequality are strongly associated with one and the other. The low-income group is vastly dependent on environmental resources, and therefore, they manipulate resources in an unsustainable way that increases environmental degradation (Finco 2009). In this situation, the two most important approaches have emerged as top priorities for researchers. On the one side, the researchers prioritize the win-to-win solution, such as the concern authorities and policymakers of a country should perform sound by reducing poverty and income inequality with worthy socioeconomic and healthier quality of the environment. Regarding this mechanical tactic, if some elements controlling degrading environment are also supportive for alleviation of poverty and reducing income inequality, counties' economic performance will be better, and they could enjoy sound environmental quality. On the other side, the trade-off situation (approach), policymakers, and concerned authorities either maintain clean and better environmental quality or embrace income inequality and poverty (Zhao et al. 2021;Wang 2014). In other words, some factors that help to control ecological vulnerability also contribute to raising the ratio of low-income people, while it further generates a trade-off situation between environment and economic targets. The main inspiration of the above is deeprooted discussion in the association between environment, poverty, and income inequality. Achieving higher economic growth is significant for the reduction of poverty if it is not linked with increasing inequality. However, for stable and long-run economic growth, energy consumption is a main and key component (Luqman et al. 2019). From the last few years, numerous economic activities have been originated to satisfy human needs and increase their standard of living. Nevertheless, the abovementioned economic determinations have raised the aggregate demand for gas, oil, and coal energy sources (fossil energy) that degrade the quality of the environment (Aydin 2019;Wang et al. 2018). In this manner, economic prosperity links with fossil energy sources and raises the living standard of low-income people, whereas, alternatively, it also causes ecological vulnerability (Wang and Feng 2017).
The last few decades are the witness that developing countries, particularly Asian developing countries, are more vulnerable to achieve Millennium Development Goals (MDGs) than developed countries. To this end, we have concentrated on developing countries from Asia because these economies are still struggling to raise the quality of life of their low-income people and reduce income inequality among them, whereas economic activities aiming to raise the quality of life of the less privileged and reduce inequality are responsible for the high demand of energy consumption and, thus, environmental degradation in Asian developing countries because the present structure of energy supply is based on fossil fuels or, in other words, non-renewable energy (Hanif et al. 2019). It can be also observed that low-income countries suffer more from trade-off situations in response to environmental mitigation efforts than high-income countries. This is because low-income people from developing countries are mainly dependent on environmental resources to fulfill their basic life needs, therefore degrading the quality of the environment (Rai and Soni 2019). Thus, it is not easy for policymakers to maintain the well-being of low-income people without compromising environmental quality. Because of this ambiguous trade-off situation, policymakers are trying to reduce environmental degradation that might influence the low-income community at large (Meinard 2021;Scolobig and Lilliestam 2016). Achieving these simultaneous objects might be a big challenge for policymakers, due to the existing trade-off situation between income inequality and environmental mitigation efforts.
Some studies investigated the relationship between income inequality and environmental degradation while they incorporated carbon emission (hereafter, CO2e) as a measure of ecological vulnerability. Contrasting from those previous studies, in the present research study, we empirically analyze the nexuses between income inequality and environmental vulnerability by using both CO2e and ecological footprint (hereafter, EFP) as a proxy for environmental pollution for the cross-countries panel of 18 Asian developing countries. The selection of utilizing both CO2e and EFP for environmental pollution is due to the reason that the net effect of income inequality might not be limited to CO2e, which is the most common and usable proxy for environmental degradation (Baloch et al. 2019;Khan and Yahong 2021). Nevertheless, income inequality may influence air pollution at a high level including natural resources, for instance, water, mining, forestry, and soil (Zaidi and Saidi 2018). Increasing the economic activities aims to facilitate low-income people to reduce poverty, which deplete the environment in the form of natural resources such as forest, water, minerals, and land, since only taking CO2e as a measure of degradation might simply provide the narrow aspect of ecology and its vulnerability and, therefore, overlook the main humanoid activities that are responsible for ecological vulnerability. By investigating the impact of major humanoid activities on the ecology, EFP includes the land's actual capacity, and hence, it is a wide-ranging and perfect proxy for the vulnerability of ecology. The EFP has been used in few recent research studies such as Abbas et al. (2021), Baloch et al. (2020b), Charfeddine and Mrabet (2017), Duro and Teixidó-Figueras (2013), Kazemzadeh et al. (2021), Mikkelson (2019), Mostafa (2010), and Solarin and Bello (2020). According to the Sustainable Development Goals (SDGs) set by the United Nations Development Program (UNDP), EFP represents the natural competence of land for sustainable economic growth (Ekeocha 2021).
Besides the background, the current study tries to answer the following research questions: What impact does CO2e and EFP have on income inequality? And what impacts do income inequality has on environmental degradation in the context of Asian developing economies? We seek the answers to these questions with the aim to examine the linkage between income inequality and environmental degradation in Asian developing economies by utilizing both CO2e and EFP. However, in the current study, we add to the existing literature in some potential ways. Firstly, to the best of our knowledge, less or even no research so far empirically analyzed the trade-off situation between income inequality and environmental degradation by utilizing both CO2e and EFP. The empirical work of Grunewald et al. (2017) has studied the trade-off between income inequality and CO2e. Our study is motivated by Grunewald et al.'s (2017) research work to use both CO2e and EFP as proxies for environmental pollution instead of only using CO2e. In this study, we have analyzed the net impact of income inequality on both CO2e and EFP as well as the actual impact of the CO2e and EFP on income inequality. Thus, our study would be a different and novel addition to the literature and would be a new contribution to the efforts for drawing attention to this empirically and theoretically ambiguous trade-off situation between income inequality and environmental degradation. Secondly, no cross-country panel study has investigated the relationship between income inequality and environmental degradation using both CO2e and EFP by considering Asian developing countries. To the best of our belief, the current research would fill the existing research gap and provide useful insight for low-income countries in Asia. Thirdly, in this study, we have applied an advanced Driscoll and Kray (D&K) econometric approach, which is reliable and provides robust results.
The rest of the paper is structured as follows: Theoretical background and review of the literature are carried out in Sections I and II, respectively. The methodology is covered in Section III. Similarly, Section IV covers the empirical results of the study along with discussion. Section V draws conclusion and suggests policy implications.

Theoretical background
Many researchers have documented that there might be inverted U-shaped association between environmental degradation and inequality. In the literature, the empirical work of Grossman and Krueger (1995) suggested an inverted U-shaped relationship between income and environmental degradation. This strong empirical evidence has confirmed the environmental Kuznets curve (EKC). The EKC was the modification of the Kuznets curve (KC) introduced by Kuznets (1955). The concept of EKC hypothesis and its possible causes such as income inequality, consumer preferences, technological progress, international trade, and governance is presented by Kaika and Zervas (2013a), while, in the revised version, Kaika and Zervas (2013b) presented a comprehensive critical review on the EKC hypothesis. The EKC hypothesis between income and many other environmental toxins has been empirically studied with inconclusive findings. In recent past literature, EKC hypothesis has mostly concentrated on CO2e, as it is considered to be the main driver of environmental degradation. The work of Hanewinkel et al. (2013) and Tol (2002) argues the environmental costs related with environmental change for developed and least developed countries. There is no exactness on how the GDP growth can decline, but there is some agreements on its indirect shocks. Determining whether the pattern of EKC hypothesis holds for global environmental changes, for instance, CO2e, has main implications for policymakers because this arguments suggests a very resilient and balanced "grow-now, clean-later" idea that has been implemented by numerous developing economies.
The EKC hypothesis pattern for environmental degradation has been mixed by many researchers such as Aslanidis and Iranzo (2009) investigate the non-homogeneous nature of relationship between income and CO2e and filed to find the existence of EKC hypothesis. On the other hand, Narayan and Narayan (2010) find this relationship in terms of both long-and short-run income elasticities of environmental degradation revealed significant empirical evidence to support the EKC hypothesis.
In recent literature, the empirical study on the EKC hypothesis is investigated by simply incorporating environmental degradation against income. However, this methodological approach suggests certain empirical and theoretical understandings about the process of economic development and environmental degradation, while its findings were inconclusive. The empirical model of EKC hypothesis does not result in a good fit for many environmental pollutants. Since the literature of EKC hypothesis has been established, it has become evident that taking income inequality into consideration is essential to understand the nexus between income and environmental degradation (Grossman and Krueger 1995;Heerink et al. 2001).
The two most popular and important theories on how income inequality could have a positive influence on the quality of environment were introduced in rapid progression. The first theory of Ravallion et al. (2000) and Heerink et al. (2001) regarding income inequality and environmental degradation refers to the theory of consumption. This theory provides a nonlinear association between environmental degradation and income inequality at household level. For instance, Cropper and Griffiths (1994) revealed that low level of household income increases aggregate demand and/or consumption for firewood that causes deforestation. Nevertheless, a higher level of household income decreases the consumption level and/or demand for firewood as other advanced forms of energy could be used. Depending on the threshold of income level, diverse behavior and manners might be observed. Additionally, income and CO2e reveal concave but positive association (Holtz-Eakin and Selden 1995). According to Ravallion et al. (2000) and Heerink et al. (2001), if the relationship is purely concave at micro level, the theory predicts an indirect and negative relationship between CO2e and income inequality for a certain income level. The tested theory of Ravallion et al. (2000) and Heerink et al. (2001) found negative nexus and supported the existence of trade-off situation between reducing CO2e and decreasing income inequality.
The second and most commonly discussed theory based on political economy regarding environmental degradation and income inequality is introduced by Torras and Boyce (1998). According to their proposed hypothesis, increasing income inequality will cause the aggregate demand of people for environmental quality. In environmental economics, the quality of environment is considered to be an ordinary good, indicating that demand will rise with increasing level of income. But those people who experience financial gain from high emitting activities might not demand for good environmental quality as their level of income rises (Rojas-Vallejos and Lastuka 2020). Another possible explanation of this theory is that widening income gap has a leading role in pro-economic growth reforms, and for that, it is not essential to consider environmental pollution. Irrespective of the detail explanations, the mechanism of political economy suggests a direct relationship between environmental degradation and income inequality. Magnani (2000) check the validity of Torras and Boyce's (1998) theory of political economy, while she also provides empirical evidence on decreasing income inequality helping to reduce environmental degradation.

Review of the literature
In the current study, we investigate the nexus between income inequality and environmental degradation in terms of EFP and CO2e. For better understanding, we present the literature review in tabulated form, whereas the first column contains authors' names and years, the second column represents country/region, the third column presents periods, the fourth column presents method/methodology, while the last and fifth column presents the main results (Table 1).

Data
For the analysis of the current study, we use cross-country panel data of 18 Asian developing economies from 2006 to 2017 (for detail, see Appendix 1, Table 5). The variables that contain the model of this study are environmental degradation, income inequality, inflation, foreign direct investment, population growth, percentage of forest area, access to electricity, and industrialization. In this study, we used both CO2e and EFP as proxies for environmental degradation, Gini coefficient as a proxy for income inequality, inflation calculated as the consumer price index (CPI), FDI measured as the inflow of foreign direct investment, population growth calculated as annual population growth rate, forest area only calculated as the percentage of forest area out of the total land area, access to electricity (% of the total population), and manufacture value added (% of GDP) as the proxy for industrialization. The data of income inequality and environmental degradation are only available for limited years and countries. The data of CO2e, income inequality, inflation, FDI, population growth, forest area, access to electricity, and industrialization are borrowed from the World Development Indicator (WDI), while the data for EFP are gathered from the Global Footprint Network (GFN). According to WDI, CO2e is calculated as the total emissions of carbon dioxide per capita in metric tons, while the EFP was introduced by Rees (1992) to measure environmental pollution because of human consumption with regenerative biological capacity. According to Rees (1992), EFP also measures the degradation of natural resources as a result of economic activities. On the other hand, EFP is a measuring scale of human demand on nature. The indicator of EFP is the summation of grazing land, forest area, cropland, fishing ground, and infrastructure footprint (Charfeddine and Mrabet 2017). The variables' definition and descriptions are shown in Table 2.
The current study explores the nexus between income inequality and environmental degradation by incorporating inflation, FDI, population growth, forest area, access to electricity, and industrialization as control variables. The choice   (Koçak et al. 2019). The selection of variables used in the study's models is interested and motivated in the spirit of Sustainable Development Goals (SGDs). SDGs aim to minimize all forms of social and economic inequalities; manage inflation; facilitate sustainable economic growth with foreign direct investment; control population growth, reducing dependency on fossil fuel energy and maximizing access to renewable and environmentally friendly energy; control deforestation to control climate change and protect the environment; and support industrialization process by encouraging renewable energy.

Econometric strategy
In order to investigate the causal relationship among the variables of the study, literature provides several econometric techniques such as generalized movement method (GMM), fully modified ordinary least squares (FMOLS), and dynamic ordinary least squares (DOLS) that assume the cross sections are fully independent. However, these assumptions might be challenged on the ground that factors such as income inequality, CO2e, and EFP lead to crosssectional dependencies. Likewise, augmented mean group (AMG) and common correlated augmented mean group (CCAMG) are applicable in the presence of non-stationarity, cross-sectional heterogeneity, and endogeneity in a series . But, these methodologies may be criticized in the case of unbalanced panels and the existence of missing values in a series. In this study, we use Driscoll and Kraay (D&K) standard error regression approach developed by Driscoll and Kraay (1998). The D&K standard error regression approach has been utilized by many researchers in similar studies, such as Knight (2014), Majeed and Mazhar (2019), and Zhang et al. (2020). The panel data might have many problems including autocorrelation, heteroscedasticity, and cross-sectional dependency. Therefore, to handle these possible issues in panel data, we prefer the D&K standard error regression approach over the abovementioned methodologies and re-analyze all the models (Jebli et al 2016; Murshed et al. 2020). It creates a standard error by taking the mean of the product of explanatory variables along with residual which is robust against cross-sectional dependency. Additionally, this approach has the most effective tools and is guaranteed if in case there are missing values in the series of panel data. Besides, D&K standard error approach is useful for balanced panel series as well as for unbalanced panel data series. Further, the D&K regression technique permits long-time dimension and flexibility because it uses a nonparametric approach. Therefore, in the current study, we incorporated a D&K standard error for pooled ordinary least squares (POLS) approach through a multiple linear regression model which can be expressed as follows: where i = 1, 2, 3, ……, N and t = 1, 2, 3,...., T (N represents panel of countries, while T represents years). y i,t represents regressand variables, and X i,t comprises all regressors (all dependent and independent variables mentioned in Table 1). The D&K standard error regression technique is the most common and suitable econometric method in the recent work of Azam and Khan (2016)

Empirical results and discussion
In our work, we applied D&K standard error regression techniques to check the nexus between income inequality and environmental vulnerability in terms of CO2e and EFP for the panel of 18 Asian developing economies. The empirical justification and estimation of this work have been analyzed in two steps. In the first step, the study has taken the environmental degradation (both CO2e and EFP) as a response variable, while in the second step, income inequality (Gini coefficient) is considered as a dependent variable. Since for the ease of interpretation, the analysis of this study is based on log (ln) values of panel data series, the coefficient of long-run elasticity of lninf, lnfa, lnfdi, lnpopg, lnace, and laindu is statistically equal to EFP, CO2e, and Gini coefficient with respect to inflation, forest area, FDI, population growth, access to electricity, and industrialization, respectively. The estimated results from D&K standard error regression models are presented in Tables 3 and 4. In model I of Table 3, we used Gini coefficient (lngini) as a measure for income inequality which is positive and significant with EFP. This suggests that a 1 percent rise in income inequality increases EFP (proxy for environmental degradation) by 1.965%. Similarly, in model II of Table 3, the coefficient of income inequality is also positive and significant with CO2e. This indicates that an increase in income inequality leads to an increase in CO 2 e (a proxy for environmental degradation) by 0.542%, whereas, in model III of Table 4, the coefficient of EFP is positive and significant with income inequality. This infers that 1 percent growth in the amount of EFP leads to a rise in income inequality by 0.120%. Likewise, the coefficient of income CO2e is also positive and statistically significant even at the 10% level. This suggests that a 1% increase in the coefficient of CO2e leads to increasing income inequality by 0.025%. These estimated results support the evidence for the existence of a trade-off relationship between income inequality and environmental protection. As for models I and II of Table 3, a positive effect of income inequality on environmental degradation is justifiable for y i,t = β0 + X i,t 1 + i Asian developing economies, because low-income people living in these regions excessively misuse natural resources in form of water, food, and energy to fulfill their basic needs and sustain their livelihood. The excessive usage of natural resources leads to environmental degradation (Baloch et al. 2020a;Ullah and Awan 2019). Another possible explanation could be that income inequality in Asian developing economies may decrease the level of education and raise the affordability to consume high-emitting sources of energy among high-income people. Therefore, lack of awareness in low-income people and high affordability of energy consumption among high-income people increase environmental degradation in the form of EFP and CO2e. As mentioned by Demir et al. (2019), income inequality in the least developed economies strengthens the industrialists and capital owners who are commonly investing in outdated and highemitted technology to increase their income and thereby degrading environmental quality. These empirical findings also emphasize the theoretically ambiguous hypothesis of Boyce (1994) that rising income inequality generates class difference (power gap) between poor and rich in a society that leads to worsening the environmental quality, because the high-income people take advantage of the environment, while the low-income people cannot take advantage because all costs are imposed on them. Our empirical results urge that reducing inequality could help to control environmental degradation by reducing EFP and CO2e. Additionally, with the wide income gap, low-income people tend to excessively exploit the environmental resources to sustain their livelihood, suggesting rising income inequality is not only a social and economic issue but also an environmental problem. Our findings are in line with the empirical results of Bae (2018) for G-20 economies, Knight et al. (2017) for developed economies, Liu et al. (2019) for provincial economies of China, Masud et al. (2018) for ASEAN 5 economies, and Zhang and Zhao (2014) for regional economies of China; conversely, our results contradict to Demir et al. (2019) for Turkey and Grunewald et al. (2017) for middle-income and low-income economies. To the best of our knowledge, this empirical contradiction with our empirical work is because the dataset we used is not different, i.e., Asian developing economies, and also we have applied a different and advanced methodological approach to obtain our empirical results.
As for models III and IV of Table 4, it infers that higher EFP and CO2e are likely to increase income inequality in Asian developing economies. Therefore, serious steps are taken to reduce environmental degradation in order to control the widening income gap. The findings of the current study are justifiable for low-income and developing economies like Asian developing countries because to reduce extreme poverty, it is essential to establish a new setup of an industrial economy. In developing economies, in most cases,  the industrialization process failed to reduce poverty; rather high-income people take the advantage of industrialization process and the cost of environmental degradation imposed on low-income people, which further increases the income gap between rich and poor. The adverse impact brought by the industrialization process in terms of ecological vulnerability makes huge losses to the socioeconomic classes and eventually reduces the capacity of policymakers to reduce poverty and income inequality (Khan and Yahong 2021). Furthermore, most of the developing economies initiate development projects to support low-income people without considering environmental degradation, since capital owners and high-income people take advantage of these development projects. As a result, they invest in industries by providing high-emitting energy sources that contribute to a rise in environmental vulnerability and thereby highincome inequality (Grunewald et al. 2017). Additionally, Asian developing economies are often facing high financial and technological and low adaptive capacity during economic activities. In order to overwhelm these constraints and maintain an economic steady flow, developing economies may compromise on the quality of the environment (Baloch et al. 2020b). The above empirical results describe a clear image of the existence of a complex trade-off situation between income inequality and environmental protection in the Asian developing economies. On the basis of the above strong empirical evidence, income inequality is harmful to environmental quality, although efforts toward reduction of poverty and income inequality lead to an increase in environmental degradation. It reveals that there are conflict and ambiguity between policies aiming to control all types of poverty (including income inequality) and environmental degradation. The present practices regarding economic and environmental policies to reduce poverty are increasing income inequality and not environmental friendly. Therefore, it is necessary to devise both economic and environmental policies that particularly consider income inequality and environmental degradation. To this end, policymakers of the selected region should educate poor as well as rich people to employ toward sustainable resource consumption, because most low-income people of the selected region are based on agriculture. Moreover, concerned authorities of these developing economies should impose strict regulations regarding the environment that could be helpful in reducing environmental degradation and also raising income inequality.
Regarding control variables used in the analysis of this study, as can be seen in model I of Table 3, inflation has a statistically significant and negative effect on environmental degradation. A 1 percent increase in the coefficient of inflation rate will degrade environmental quality in terms of EFP by 0.02 percent. Similarly, in model II of Table 4, inflation has a negative and statistically significant relationship with CO2e. It suggests that a 1 percent increase in the inflation rate will degrade the environment by 0.008 percent in Asian developing economies, whereas in models III and IV of Table 4, the inflation rate has a positive and statistically significant association with income inequality. A 1 percent increase in the coefficient of inflation is contributing to an increase in income inequality by 0.06 and 0.05 percent, respectively. The positive relationship between inflation and environmental degradation suggests that increasing inflation reduces environmental degradation particularly in the short run, as suggested by Khan (2019), Khan and Yahong (2021), and Malik et al. (2020) for Pakistan. In terms of FDI, it benefits economies in multiple ways as it not only allows technology spillover and knowledge sharing but also improves the capacity of employment and production (Oxelheim and Ghauri 2008). However, the findings of models I and II of Table 3 suggest a positive and statistically significant relationship between FDI and environmental degradation, while the coefficient of FDI is negative and statistically significant with income inequality as shown in models III and IV of Table 4. The findings indicate that FDI can increase environmental degradation, while the greater ratio of FDI can help to reduce income inequality by increasing the equality of life. Our findings are in line with the empirical work of Malik et al. (2020) for Pakistan.
Next, the coefficient of access to electricity (lnace) is statistically significant and positive with both EFP and CO2e, while it has an insignificant impact on income inequality. It indicates that access to electricity can increase environmental degradation and can play a vital role in achieving Sustainable Development Goals (SDGs), which help to decrease inequality and poverty. However, the immense usage of cheap energy consumption and massive utilization of fossil fuels badly affect the quality of environmental quality. Our empirical findings coincide with the work of Danish (2020) for sub-Saharan African countries and Malik et al. (2020) for Pakistan. The impact of the rest of the control variables on environmental degradation and income inequality is as follows: (i) Population growth rate (lnpopg) has a significant positive effect on both EFP and CO2e, while it has a negative and statistically significant relationship with income inequality. (ii) Forest area (lnfa) has a significant and negative relationship with environmental degradation, while it has a positive and statistically significant impact on income inequality. (iii) Industrialization and value added (lnindu) have a negative relationship with environmental quality and positive relationship with income inequality as shown in Tables 3 and 4. Here, it is worth mentioning that this study has incorporated two proxies for environmental degradation (ecological footprints/EFP and carbon emissions CO2e) to check the robustness of our analysis. The findings obtained from D&K regression by using EFP and CO2e are consistent with each other which confirm the reliability and validity of our analysis.

Conclusion and policy implications
Environmental quality and income inequality are linked with each other and should be taken into account to achieve Sustainable Development Goals (SDGs). The current research study analyzed the relationship between environmental degradation and income inequality by using the data of 18 Asian developing economies from 2006 to 2017. The study has also utilized as many as possible control group variables such as inflation, foreign direct investment (FDI), access to electricity, population growth, forest area, and industrialization. The empirically ambiguous findings reveal the existence of a trade-off situation between environmental protection and income inequality. It infers that measures taken for environmental protection lead to increase income inequality, and therefore, addressing this concern at its top priority has become more challenging and complex. Moreover, population growth is helpful to control income inequality but is detrimental to the quality of the environment. This contrast between environmental protection and income inequality creates a contradictory and alarming situation for the achievement of United Nations (UN) targeted development goals. When aiming to protect the environment, the socioeconomic well-being of the poor people might be compromised; thus, government officials of targeted economies should take serious steps by policy implementation that economic well-being along with ecological protections is the crucial goal of development. The most crucial and possible solution is to implement win-to-win policy that upholds environmental protection along with people's economic well-being and ensures other important development goals. For example, FDI plays a vital role in increasing economic growth and restoring the economic well-being of the poor people along with controlling income inequality but at the high environmental costs. Therefore, introducing environmental-friendly energy sources to attract foreign investors can reduce environmental costs along with controlling income inequality and poverty. Additionally, estimated findings reveal that both EFP and CO2e measures of environmental degradation lead to increase income inequality. It infers that controlling environmental quality would be helpful to reduce income inequality. Therefore, government authorities and policymakers of investigated countries should invest more and apply mitigation policies and educate people to sustainably utilize natural resources. At the same time, concerned authorities should implement strict regulations regarding environmental protection by considering the possible impact on people. Furthermore, the empirical findings of the current study have significant policy implications as some developing countries around the globe are currently making efforts to address issues surrounding both environmental degradation and economic inequalities. By this, we infer that higher income inequality leads to degrading environmental quality, while reducing income inequality would be supportive for the environment. Therefore, the concerned authorities of the investigated region should invest more in low-income people (by providing equal education and job opportunities) to sustainably utilize natural resources. In addition, the policymakers of investigated countries need to formulate environmental protection regulations by considering the high interest of low-income people. Particularly, the policy that could address this situation effectively is to impose a toxic emissions tax that would incentivize emissions reduction in the most feasible way, while the allocation of this tax revenue could be utilized to improve public services used by low-income people. For example, Boyce (2018) suggests some empirical evidence to support this type of policy implications. According to him, revenue-neutral emission tax with equal per-head dividends would be a progressive policy with the top quintile paying more for the emission tax than they get from the repayment. Additionally, the empirical results reveal that the inflation rate increases income inequality in Asian developing economies. This infers that controlling the general pricing level can decrease income inequality. Therefore, the policymakers of the investigated region should also formulate a pricing control mechanism to control the unequal distribution of income. Likewise, FDI and access to electricity are good to control income inequality but bad for the environment; thus, the governments of the investigated countries should ensure effective environmental protection policies along with redistributive policies to control income inequality and environmental degradation.
Although the empirical results have vast importance toward policy implications, not without some limitations, that needs to highlight and extend for future studies. The current research incorporated the Gini coefficient as a measure of income inequality, while EFP and CO2e were utilized for environmental degradation, respectively. Constructively, this study can be extended to utilize other measures for environmental degradation and income inequality and can verify whether the empirical results obtained from this study are consistent and robust to other indicators of environmental degradation and income inequality. In addition, we empirically analyzed the causal relationship between income inequality and environmental pollution by incorporating CO2e and EFP in Asian developing economies. However, this research opens the door for future researchers to analyze the nexus between income inequality and environmental degradation in other emerging and developed countries. Future