China's commitment to peak carbon emissions before 2030 necessitates effective and equitable carbon emission reduction policies. The establishment of a national carbon emission trading scheme (ETS) is a key element of this strategy, but the design of the scheme, particularly the mechanism for revenue recycling, significantly impacts its overall effectiveness and distributional consequences. This research investigates the potential economic distortions and the efficacy of various revenue-neutral carbon ETS designs in China, focusing on two primary revenue recycling strategies: government subsidies and value-added tax (VAT) reductions. The study's purpose is to provide policymakers with critical insights into the optimal allocation of carbon market revenues to minimize adverse economic effects while promoting social equity and environmental sustainability. The importance of this research stems from the need to balance the environmental imperative of carbon reduction with the socio-economic considerations of ensuring fair and efficient resource allocation in a developing economy like China. The choice between subsidies and VAT relief, as well as the targeted sectors for revenue redistribution, will directly influence the outcome of China's carbon reduction efforts.

Existing literature extensively debates the effectiveness and distributional impacts of carbon pricing mechanisms. Studies like those by Beck et al. (2015) and Goulder et al. (2019) examine the impact of carbon taxes and revenue recycling on household income in different contexts. Research by Brenner et al. (2007) specifically addresses the distributional impacts of carbon charges and revenue recycling in China. Other studies, such as those by Liu (2016) and Zhou & Pan (2019), explore the incentive effects of government subsidies and tax incentives on various economic activities. This paper builds on this existing body of knowledge by specifically modeling the dynamic effects of different revenue recycling mechanisms within the context of China's specific economic structure and carbon reduction targets, employing a dynamic CGE model to assess the nuanced impacts of different policy choices. This approach allows for a more comprehensive and contextually relevant assessment than previous studies.

This study employs a dynamic computable general equilibrium (CGE) model to simulate the impacts of different carbon ETS designs and revenue recycling strategies in China. The model incorporates detailed representations of China's economy, including various production sectors, household income groups, and energy consumption patterns. The baseline scenario (BAU) assumes no policy intervention. The key policy scenarios involve the implementation of a carbon ETS with revenue recycling through either government subsidies or VAT reductions. These policies are designed to be revenue-neutral, meaning that any revenue generated by the ETS is fully redistributed. The model accounts for various industries, dividing them into ETS-covered (power, aviation, steel, chemicals, building materials, petrochemicals, non-ferrous metals, and paper) and non-ETS-covered sectors. The model simulates a time horizon encompassing the period from 2018 to 2035 to capture the long-term effects of policy changes and incorporates different scenarios of revenue allocation to production sectors (ETS-covered, non-ETS-covered, or all sectors) under both subsidy and VAT reduction schemes. Data for the model was derived from various sources, including the National Bureau of Statistics and other relevant publications. The model accounts for the gradual expansion of the ETS, starting with the electricity sector in 2021 and expanding to the eight specified sectors from 2023 onward. A carbon intensity decline rate is incorporated to simulate the emission reduction targets. This decline rate is gradually increased throughout the period from 2021 to 2035. The model is calibrated using data representing the Chinese economy and is then simulated under various policy scenarios to assess the economic, environmental, and distributional impacts of the proposed revenue recycling schemes.

The CGE model simulations reveal several key findings: 1. **GDP Impact:** Implementing a carbon ETS leads to a decline in real GDP, but this decline is mitigated when carbon market revenues are redistributed to the production sector via subsidies or VAT reductions. Subsidies prove more effective for high-carbon industries, while VAT reductions benefit non-ETS covered industries. 2. **Government Revenue:** The single carbon ETS scenario (S1) shows the highest increase in government revenue due to allowance sales. However, when revenue is recycled, government revenue declines compared to S1. The decline is more substantial with subsidies than with VAT cuts. The least decline in government revenue occurs when revenues are returned to ETS-covered industries. 3. **Sectoral Output:** The coal sector experiences substantial output decline under all ETS scenarios. However, subsidies and VAT reductions boost output in oil, gas, and ETS-covered sectors. VAT reductions are more effective than subsidies in stimulating growth in most sectors, especially oil and gas. Redirecting revenue to non-ETS-covered sectors leads to the least decline in coal output and the least growth in ETS-covered sectors. 4. **Energy Consumption:** Total energy consumption decreases under all ETS scenarios compared to the BAU. Subsidies and VAT cuts increase energy consumption compared to the single ETS scenario, but to differing degrees. VAT cuts incentivize greater reduction in overall energy consumption by allowing industries to retain more of the funds generated by reduced energy consumption. Redirecting revenue to ETS-covered sectors leads to the largest decrease in overall energy consumption. 5. **Carbon Emissions:** The carbon peak is achieved in 2029 in the S1 scenario (single ETS). Recycling revenue to ETS-covered sectors maintains the 2029 peak. Recycling revenue to non-ETS-covered or all sectors delays the peak to 2030. VAT reductions are more effective in reducing carbon emissions than subsidies. 6. **Income Inequality:** The income Gini coefficient increases in the BAU scenario. A single ETS reduces inequality, primarily due to government redistribution. However, the effect of revenue recycling depends greatly on the chosen sectors and method. Returning carbon market revenue to ETS-covered industries through VAT relief is most effective in reducing income inequality, while returning revenue to non-ETS-covered sectors through VAT relief is least effective, and even worsens the inequality. The effect of subsidies on income inequality is milder compared to VAT relief.

The findings highlight the importance of careful policy design in implementing a revenue-neutral carbon ETS. The choice of revenue recycling mechanism and the targeted sectors significantly influence the economic, environmental, and distributional consequences. The study demonstrates that simply implementing a carbon ETS is not sufficient to achieve both environmental sustainability and social equity. Strategic revenue recycling is crucial. The superior performance of VAT relief targeted at ETS-covered industries in reducing both emissions and income inequality suggests that this approach might be the most desirable policy option for China. These results provide significant implications for other developing nations contemplating similar emission reduction strategies, especially those with similar income distributions. Furthermore, the differing impacts of subsidies and VAT reductions suggest a nuanced approach is necessary, tailoring the policy to the specific characteristics of different sectors and acknowledging the diverse responses to different incentive mechanisms.

This study underscores the importance of strategic revenue recycling in achieving China's carbon emission reduction goals while ensuring economic stability and social equity. The dynamic CGE model demonstrates that allocating carbon market revenues to ETS-covered industries through VAT relief is the most effective approach to mitigating the economic consequences of carbon pricing, fostering a sustainable energy transition and reducing income inequality. Future research could explore the impacts of incorporating additional policy instruments, such as energy efficiency standards, alongside carbon pricing, and further investigate the long-term dynamic effects of these combined policies on various economic and social indicators.

The study's limitations stem primarily from the inherent assumptions and simplifications of the CGE model. The model's accuracy depends on the quality and availability of input data, and certain aspects of the complex Chinese economy may not be fully captured. Future research should incorporate more granular data and potentially refine the model to account for factors such as technological innovation, behavioral changes, and regional disparities in greater detail. Furthermore, the model focuses on macroeconomic indicators, and more micro-level analysis could provide a richer understanding of the distributional impacts across various demographics within China. The assumption of revenue neutrality might not fully reflect real-world policy implementation.