Asia's remarkable economic growth (5-5.5% annually) in recent decades has been accompanied by a rise in income inequality since the early 1990s, a concern for economists and policymakers due to its implications for sustainable development. Income inequality's effect on carbon dioxide (CO2) emissions is a key area of investigation. While some studies suggest a positive correlation between income inequality and environmental degradation, others find a negative correlation or no relationship. The role of human capital in this relationship is also complex; investment in human capital can reduce income inequality and CO2 emissions, but can also increase emissions by stimulating economic growth. This study aims to clarify the interplay between human capital, income inequality, and CO2 emissions in Asia (2007-2020). Understanding this relationship is crucial for developing effective policies to balance economic growth with environmental protection in the region, especially given China's leading role in global CO2 emissions and the link between social issues like income inequality and environmental quality. The study makes three main contributions: First, showing the combined effect of income inequality and human capital investment on environmental degradation; Second, analyzing the impact of other factors on CO2 emissions; Third, proposing policy implications for sustainable economic development in Asia.

The study begins by defining income inequality using Kuznets's (1955) and Fletcher and Guttmann's (2013) concepts, emphasizing the Gini coefficient as a key measure. The theoretical framework discusses Boyce's (1994) mechanism of how income inequality affects CO2 emissions: the rich have more power to benefit from environmentally damaging actions, increasing demand for luxury goods; and the poor have higher marginal propensity to emit due to affordability constraints. Ravallion et al. (2000)'s concept of marginal propensity emissions (MPE) is mentioned; along with Veblen's (2009) theory of conspicuous consumption driving energy use. The concept of human capital is examined from Smith (1776) through Mincer (1958), Becker (1964), and Schultz (1961) up to more recent definitions. The literature review highlights the role of human capital in economic development and its potential to influence environmental quality through technological innovation, improved energy efficiency, and increased environmental awareness. The review then presents mixed empirical evidence on the income inequality-CO2 emissions relationship. Several studies find a positive correlation, while others find a negative correlation or no significant relationship. The effect of human capital on CO2 emissions is also shown to be mixed, with some studies finding a positive relationship and others a negative one. Finally, some studies show that human capital can reduce income inequality, and vice versa. This review concludes by highlighting the scarcity of studies analyzing the simultaneous impact of human capital and income inequality on CO2 emissions, leading to the study's focus on this area.

This study uses balanced panel data from 46 Asian countries over the period 2007-2020, obtaining data from the World Bank, Worldwide Governance Indicators, Our World in Data, and the International Monetary Fund. The dependent variable is the natural logarithm of CO2 emissions (LnCO2). The main independent variables are the Gini coefficient (GINI) measuring income inequality and three measures of human capital: gross enrollment ratios for primary (HC1), secondary (HC2), and tertiary (HC3) education. Control variables include per capita GDP (LnGDP), foreign direct investment (FDI), renewable energy (ENG), population (LnPOP), service sector output (S), agricultural sector output (AG), manufacturing sector output (MN), trade openness (TO), total investment (INV), and government expenditure (GEX). The study uses several econometric techniques: Initially, Pooled OLS, FEM, and REM are considered; with the Hausman test used to select the most appropriate model. Subsequently, FGLS addresses heteroscedasticity and autocorrelation. Finally, a two-step system GMM estimator addresses endogeneity. The model is specified as LnCO2it = β₀ + β₁Gini + β₂HC1 + β₃HC2 + β₄HC3 + β₅LnGDP + β₆FDI + β₇ENG + β₈S + β₉AG + β₁₀MN + β₁₁LnPOP + β₁₂GEX + β₁₃INV + β₁₄TO + εit, and further extended to include interaction terms (Gini*HC1, Gini*HC2, Gini*HC3) to test for moderation effects of human capital on the relationship between income inequality and CO2 emissions. Before regression analysis, panel unit root tests (ADF, LLC, PP) and a cross-sectional dependence test (Pesaran 2004) are performed. The Pesaran (2003) CADF test is also used to address cross-sectional dependence in the unit root tests. Stata 17.0 software was used for data analysis.

Panel unit root tests (ADF, LLC, PP) confirmed the stationarity of all series at the first-order difference. The cross-sectional dependence test (Pesaran 2004) and the CADF test (Pesaran 2003) revealed cross-sectional dependence among the countries. The Hausman test suggested FEM as the most suitable model for model (1), though it exhibited heteroscedasticity and autocorrelation. These were addressed using the xtgls command with panel-corrected standard errors. Model (2), employing GMM, identified 11 significant variables influencing CO2 emissions: GINI, GDP, HC1, HC3, ENG, LnPOP, S, AG, GEX, TO, and INV. HC2, MN, and FDI were not significant. A high correlation (0.959) between current and past CO2 emissions suggests the lasting influence of past emissions on current climate change. The Hansen test confirmed the absence of autocorrelation in the GMM model. Crucially, model (3) revealed a positive and significant coefficient for Gini*HC3 at the 1% level, indicating that the tertiary-level gross enrollment ratio moderates the impact of income inequality on CO2 emissions; meaning that increases in tertiary education enrollment reduces the negative impact of income inequality on the environment. The findings show that income inequality (GINI) has a positive and significant impact on CO2 emissions. While human capital (HC1 and HC3) exhibits a positive relationship, the interaction effect reveals that only tertiary enrollment (HC3) plays a significant role in mitigating income inequality's negative environmental effects. Other significant factors include GDP (positive), renewable energy (negative), population (positive), services (negative), agriculture (negative), government expenditure (negative), trade openness (negative), and total investment (negative).

The positive impact of income inequality on CO2 emissions aligns with the expectation that unequal wealth distribution hinders effective climate change mitigation policies, due to lack of political will and resource allocation priorities for low-income groups. The positive relationship between human capital and CO2 emissions suggests that human capital investments stimulate economic growth, which leads to increased energy consumption and emissions. However, the moderating effect of tertiary education (HC3) is explained by improved job opportunities and higher incomes for those with higher education. This reduces income inequality and facilitates environmentally friendly consumer choices. The negative impact of renewable energy (ENG), consistent with earlier findings, highlights its potential for mitigating climate change. The results also suggest that population growth (LnPOP), though not necessarily a direct cause of increased emissions, puts pressure on resource consumption and infrastructure, leading to environmental damage. In contrast, the services sector (S) and agricultural sector (AG) are shown to have negative effects on CO2 emissions, indicating the potential for environmental stewardship in these sectors. The negative relationships between CO2 emissions and trade openness (TO), total investment (INV), and government expenditure (GEX) suggest that these factors contribute to environmental protection through investment in green technologies or environmental policies.

This study confirms the positive impact of income inequality and human capital on CO2 emissions in Asia (2007-2020). It highlights the importance of equitable income distribution and investments in tertiary education for mitigating climate change. Policy recommendations include inclusive fiscal policies, investments in social security, and job creation initiatives; promoting green agriculture, sustainable population management, and increased renewable energy use. The study's limitations include the use of only three human capital indicators and a focus on 46 Asian countries. Future research can expand the scope and depth of analysis.

The study's limitations include the use of only three composite human capital development indicators; potentially overlooking the effects of other factors like adult education, skill levels, and health. The focus on 46 Asian countries limits the generalizability of findings. Future studies could incorporate a broader range of human capital measures and expand the geographical scope to include countries globally, enabling comparisons between developing and developed nations.