Climate change is expected to exacerbate wealth inequality, and climate policies may further worsen this disparity. While there's growing awareness of the link between climate change and inequality, research on the impact of climate policies on inequality remains limited. This study addresses this gap by focusing on China, a major emitter, and employing a CGE model to quantify the inequality impacts of various carbon tax scenarios. The poor often spend a larger proportion of their income on pollution-intensive goods, making them disproportionately vulnerable to climate policies that raise energy prices. Existing literature often simulates regressive climate policies, highlighting the need to analyze the distributional effects of such policies to ensure optimal policy design. The study further incorporates the concept of relative utility, acknowledging that individual well-being depends not only on absolute income but also on relative income compared to a reference group. Existing research indicates a negative correlation between relative utility and subjective well-being. This study links inequality impacts of climate policies to behavioral studies and measures welfare impacts using relative utility, analyzing how tax revenue recycling influences inequality.

Previous research highlights the growing concern about climate change's impact on generational inequality and the potential for climate policies to exacerbate existing inequalities. Studies show that energy inaccessibility among the poor can hinder the political feasibility of climate reforms. Researchers advocate for climate policies to curb emissions, but these policies, particularly those that increase energy prices, can negatively affect the poor disproportionately. Existing literature acknowledges that climate policies might have regressive effects, but detailed analysis on their inequality impacts remains scarce. The relationship between income inequality and greenhouse gas emissions is complex and multi-layered. Ignoring inequality impacts can lead to biased policy evaluations and potentially weaken the effectiveness of climate policies. Studies also show that relative utility (comparing one's income to a reference level) is negatively correlated with subjective well-being, highlighting the importance of considering psychological factors when evaluating climate policy. Little to no research exists on the psychological effects of climate policies, leaving a gap this study aims to address.

This paper employs a Computable General Equilibrium (CGE) model, a tool based on Walrasian general equilibrium theory, to analyze the inequality impacts of carbon taxes in China. The model uses a social accounting matrix (SAM) based on the 2015 Chinese Input-Output Table. The Chinese economy is disaggregated into 29 sectors, with the electricity sector further disaggregated into nine subsectors to account for variations in emissions across different generation technologies. The model incorporates a dynamic recursive structure to simulate changes over time (2015-2030), considering parameters such as population, prices, energy consumption growth, output growth, and capital accumulation. The CGE model includes four economic entities: households, enterprises, foreign entities, and the government. Households are categorized into three income groups (low, mid, and high) based on the 2013 Chinese Household Income Project (CHIP). The model considers household income and consumption distribution, making it possible to assess the effects of carbon taxes on different income groups. The study analyzes the impacts of climate damage on various income groups using an income elasticity of damage parameter (ξ). Three scenarios were developed: positive ξ (climate damages proportional to income), zero ξ (damages independent of income), and negative ξ (damages inversely proportional to income). The model assesses the impact of various carbon tax rates (1%, 2%, 3% of non-renewable energy consumption) on the Palma ratio (a measure of inequality) and relative utility (a measure of subjective well-being). Twelve scenarios are simulated by combining the different tax rates with various tax payment schemes (high-income households only, proportional to income, independent of income) and tax revenue recycling methods (government, households evenly, low-income households only, enterprises). Relative utility is calculated using a formula derived from the literature, considering both absolute and relative income. A sensitivity analysis is conducted to assess the robustness of the results.

The study reveals that the income elasticity of damage parameter (ξ) significantly influences inequality. A positive ξ (high-income households bearing most damages) leads to the lowest inequality, while a negative ξ (low-income households bearing most damages) results in the highest inequality. Regardless of the ξ value, carbon taxes generally reduce inequality due to higher tax burdens on high-income households, who have a larger share of income from energy sectors. The distribution of tax payments also significantly affects inequality. Taxation focused on high-income households results in lower inequality, while income-independent taxation leads to the highest inequality, exacerbating the existing disparities. The method for recycling tax revenues plays a major role in inequality outcomes. Recycling revenues to low-income households substantially decreases inequality compared to recycling them to other groups, while recycling to enterprises has negligible effects on inequality. The study further finds that absolute income is a more important determinant of relative utility than income inequality. Higher tax rates lead to higher relative utility (in absolute value), even if inequality decreases. The sensitivity analysis indicates that the results are relatively robust to changes in elasticity parameters.

The findings highlight that the design of carbon taxes is crucial in determining their distributional effects. While carbon taxes can reduce inequality under certain conditions, such as focusing tax payments on high-income households and recycling revenues to low-income households, they can also worsen inequality if poorly designed. The study's emphasis on relative utility provides a nuanced perspective on the welfare implications of carbon tax policies. The finding that absolute income strongly influences relative utility indicates that policies aiming to mitigate negative feelings about inequality should prioritize overall economic growth. This study contributes to the existing literature by quantitatively assessing the effects of different carbon tax designs and emphasizing the role of relative utility in evaluating the policy's social impacts. The results offer practical guidance to policymakers in China, highlighting the importance of carefully considering the distributional implications of carbon taxes when designing and implementing climate change mitigation strategies.

This study provides crucial insights into the inequality impacts of carbon taxes in China. The results show that the effects on inequality depend significantly on the design of the tax policy and the distribution of climate change impacts. The findings suggest that focusing tax payments on high-income households, recycling revenues to low-income households, and policies that increase national income can mitigate the negative social impacts of carbon taxation, promoting equitable climate policy implementation. Future research should explore the effectiveness of alternative climate policies and investigate the long-term dynamics of income inequality and climate change impacts.

The study relies on a CGE model, which involves simplifying assumptions about the economy. Data limitations, particularly the reliance on the 2013 CHIP data for household income distribution, may affect the accuracy of the results. The model's projection of future economic growth and energy consumption may differ from actual outcomes, affecting the accuracy of long-term predictions. Additionally, the study focuses solely on carbon taxes and does not examine the interplay with other climate policies. The model's assumption about how the representative enterprise passes on the cost of higher electricity generation to end-users might not accurately reflect real-world dynamics.