Impact of income inequality on climate change in Asia: the role of human capital

Asia has experienced rapid economic growth (around 5–5.5% annually), alongside rising income inequality since the early 1990s, raising concerns for sustainable growth. Prior empirical findings on the relationship between income inequality and environmental degradation (CO₂ emissions) are mixed—some find that inequality worsens environmental quality, others report negative or inconclusive links. Human capital is also linked to both income inequality and CO₂ emissions: investment in education can reduce inequality and potentially lower emissions through technology and efficiency, though it may also raise emissions via growth effects. Given Asia’s significant contribution to global emissions (e.g., China) and the limited continental evidence on short- and long-run dynamics, the study investigates how income inequality affects CO₂ emissions in Asian countries while accounting for the role of human capital. Using panel data for 46 Asian countries from 2007–2020, the paper applies system GMM to address endogeneity and dynamics. Contributions: (1) evidence that rising income inequality and investment in human capital have exacerbated environmental degradation in Asia; (2) identification of other drivers of CO₂ emissions (renewable energy, economic growth, population, sectoral outputs, trade openness, government expenditure, total investment); and (3) policy implications for sustained, greener growth in Asia.

Theoretical framework: Income inequality refers to disparities in the distribution of income and wealth across individuals or groups. The Gini coefficient, derived from the Lorenz curve, is a common measure, ranging from 0 (perfect equality) to 1 (perfect inequality). Mechanisms linking inequality to environmental degradation include power asymmetries favoring environmentally harmful activities, differential marginal propensity to emit (the poor may have higher MPEs due to affordability of low-emission goods), and status-driven conspicuous consumption increasing energy use. Human capital, conceptualized as skills and knowledge acquired through education and training, is central to development and may influence environmental outcomes by fostering environmental preferences and technological innovation that improve energy efficiency. However, human capital can also raise emissions by stimulating economic growth. Empirical evidence: Studies report mixed links between inequality and CO₂ emissions—positive (e.g., US, China, Sub-Saharan Africa; various panel approaches), negative (context-dependent effects in Indonesia; OECD vs non-OECD), and time-varying or mechanism-based effects (via R&D, institutional quality). Evidence on human capital often shows it reduces emissions via awareness and technology adoption, though some find positive effects through growth channels; effects may vary by sector and time horizon. Human capital is also generally found to reduce income inequality. Research gap: Most prior work examines the separate impacts of income inequality or human capital on emissions, with few studies analyzing their joint and interactive effects, and limited multi-country Asian evidence. This study addresses these gaps by modeling both factors simultaneously for a broad Asian panel and testing moderation by education levels.

Data and variables: Balanced panel of 46 Asian countries, 2007–2020 (644 observations). Sources: World Bank (WB), Worldwide Governance Indicators (WGI), Our World in Data (OWID), International Monetary Fund (IMF). Dependent variable: CO₂ emissions (LnCO2; natural log of tons). Key independents: income inequality (GINI, 0–1 or 0–100%), human capital measured via enrollment ratios—primary (HC1), secondary (HC2), tertiary (HC3). Controls: per capita GDP (LnGDP), foreign direct investment (FDI, % of GDP), population (LnPOP), renewable energy share in electricity (ENG), sectoral outputs (services S, agriculture AG, manufacturing MN; shares of GDP), trade openness (TO, exports+imports over GDP), total investment (INV, % of GDP), government expenditure (GEX, % of GDP). Most variables are in percentages; CO₂, GDP per capita, and population are in natural logs. Econometric approach: Initial panel unit root tests (ADF, LLC, PP) and cross-sectional dependence (Pesaran CD). Second-generation CADF tests account for cross-sectional dependence. Estimation proceeds with pooled OLS, fixed effects (FEM), random effects (REM), and FGLS; Hausman and diagnostic tests guide model choice and corrections for heteroskedasticity/autocorrelation. To address endogeneity and dynamics (N=46 > T=14), two-step system GMM is applied in Stata 17. Dynamic model includes lagged dependent variable (L.LnCO2). Interaction terms (Gini*HC1, Gini*HC2, Gini*HC3) test whether human capital moderates the inequality–emissions relationship. Model diagnostics include AR(1)/AR(2) tests and Hansen test for instrument validity.

- Unit roots and cross-sectional dependence: Series are stationary at first differences at 1% (ADF, LLC, PP). Cross-sectional dependence is present; CADF tests corroborate stationarity accounting for dependence. - Baseline results (static models) indicate several significant drivers; Hausman favors FEM, but heteroskedasticity/autocorrelation necessitate FGLS. - System GMM (preferred dynamic model): lagged CO₂ emissions are highly persistent (coefficient ≈ 0.959, 1% significance). AR2 test p>0.05 and Hansen test p=1.000 indicate no second-order serial correlation and valid instruments. - Significant determinants of CO₂ emissions (GMM): • Income inequality (GINI): positive and significant—higher inequality increases emissions. • Human capital: HC1 (primary enrollment) and HC3 (tertiary enrollment) positively associated with emissions; HC2 (secondary) not significant. • Economic growth (LnGDP): positive and significant. • Renewable energy (ENG): negative and significant—greater renewable share reduces emissions. • Population (LnPOP): positive and significant; a 1% increase in population is associated with about 1.824% higher CO₂ emissions. • Sectoral outputs: services (S) negative; agriculture (AG) negative; manufacturing (MN) not significant in GMM. • Trade openness (TO): negative and significant—greater openness reduces emissions. • Government expenditure (GEX): negative—higher public spending associated with lower emissions. • Total investment (INV): negative and significant; a 1% rise in total investment is associated with about 0.432% lower CO₂ emissions. • FDI: not significant. - Moderation by human capital: Among interactions, only Gini*HC3 is significant (positive at 1%), interpreted as tertiary education attenuating the positive effect of inequality on CO₂ emissions—higher tertiary enrollment reduces the inequality–emissions impact. - Summary: Rising income inequality and (at lower levels/aggregate enrollment measures) human capital investments have coincided with higher emissions, while tertiary education mitigates inequality’s adverse environmental effect. Renewable energy, services and agriculture shares, trade openness, government spending, and total investment contribute to emissions reduction.

The findings indicate that greater income inequality elevates CO₂ emissions, consistent with theories emphasizing power asymmetries, higher marginal propensity to emit among the poor, and status consumption. Human capital’s direct positive association with emissions (via primary and tertiary enrollment) likely reflects growth and urbanization channels that increase energy demand in the short to medium run; yet tertiary education plays a mitigating role on the inequality–emissions nexus, suggesting that advanced education improves job prospects, reduces inequality, and fosters environmental awareness and adoption of cleaner technologies. Economic growth and population expansion increase emissions, aligning with EKC theory’s rising segment and reflecting Asia’s development stage and demographic scale. Conversely, a larger renewable energy share reduces emissions, as do greater services and agriculture shares—potentially due to cleaner activity mixes and green practices in agriculture. Trade openness may facilitate access to cleaner technologies and induce competitive efficiency gains, lowering emissions. Higher government expenditure and total investment are associated with reduced emissions, consistent with public and private spending on environmental protection and green infrastructure. Overall, the results address the research question by demonstrating that inequality worsens environmental outcomes in Asia, but investments in higher education can moderate this relationship, and a set of complementary policies (renewables, openness, green-oriented spending and investment) can help decouple growth from emissions.

The study shows that in Asian countries (2007–2020), income inequality and human capital (primary and tertiary enrollment) are associated with higher CO₂ emissions, while tertiary education weakens the adverse impact of inequality on emissions. Other determinants include positive effects of GDP and population, and mitigating effects from renewable energy, larger services and agriculture shares, trade openness, total investment, and government expenditure. Policy recommendations include: adopting inclusive fiscal policies to redistribute income; strengthening labor market institutions to expand quality employment; scaling investment in human capital—especially higher education—to reduce inequality and support clean technology adoption; advancing green growth strategies, including renewable energy deployment and cleaner production; promoting green agriculture; and implementing population and urban management policies to ease environmental pressures. These measures can support sustained economic growth while mitigating climate change impacts in Asia.

The analysis measures human capital using only three enrollment-based indicators (primary, secondary, tertiary), omitting other dimensions such as adult education, skills, or quality of education. The geographic scope is limited to 46 Asian countries over 2007–2020, which may constrain generalizability. Future research could incorporate broader human capital metrics, extend coverage globally, and compare impacts across development levels to deepen understanding of the inequality–human capital–emissions nexus.