Climate change, driven by greenhouse gas (GHG) emissions, poses a significant global challenge. China, as the world's largest carbon emitter, has committed to achieving "peak carbon" by 2030 and "carbon neutrality" by 2060. A key policy instrument in this effort is the carbon emissions trading market (CETM), a market-based mechanism designed to incentivize low-carbon development. Since 2011, China has implemented CETM pilot programs in several provinces. While existing literature explores the effects of these programs on carbon emission reduction and green innovation, the impact on overall economic development and stability remains debated, with mixed results reported. This study addresses this gap by investigating the influence of China's carbon trading pilot policies on both economic stability and high-quality economic development, acknowledging the government's shift towards a high-quality economic model. The study utilizes a quasi-natural experiment design, employing a double-difference model to analyze panel data from 31 Chinese provinces from 2005 to 2021. This approach allows for the isolation of the specific impact of the carbon trading pilot programs.

The theoretical basis for carbon trading markets stems from externality theory, particularly the work of Pigou and Coase, emphasizing the need for market mechanisms to address negative externalities like pollution. Existing literature shows a mixed impact of carbon trading pilot policies on economic development. Some studies highlight positive effects on green innovation, export competitiveness, and regional economic growth, improved total factor productivity, and enhanced energy efficiency. Others report negative effects, particularly on short-term economic growth in certain sectors. The heterogeneity of these impacts across different regions and industries is also noted, emphasizing the need for region-specific policies. These contrasting findings underscore the complexity of the relationship between carbon trading and economic performance, highlighting the need for further investigation.

This study employs a difference-in-differences (DID) approach as a quasi-natural experiment to evaluate the effects of China's carbon trading pilot programs. Panel data covering 31 Chinese provinces from 2005 to 2021 were utilized. The core explanatory variable is a dummy variable (did) representing the implementation of the carbon trading pilot policy. Economic fluctuations (EF) are measured using the three-year rolling standard deviation of GDP growth rates. High-quality economic development (HQD) is measured using an index constructed from indicators representing innovation, coordination, green development, openness, and sharing, employing the entropy-weighted TOPSIS method. Control variables included investment in fixed assets, infrastructure (per capita road area), human capital (university enrollment rate), industrial enterprises (log of number of industrial enterprises above designated size), and employment (unemployment rate). Two main regression models were used: Equation (2) for the effect on economic fluctuations (EF) and Equation (3) for the effect on high-quality economic development (HQD). The models incorporate provincial and year fixed effects. Robustness checks included parallel trend tests and placebo tests. Heterogeneity analysis was conducted by separating the provinces into three regions (East, Central, and West) for separate regressions.

The baseline regression results (Table 3) reveal a significant positive effect of the carbon trading pilot program on economic volatility (EF), suggesting a short-term negative impact on economic stability. This effect remained robust even after controlling for various factors. Infrastructure development positively impacted economic stability, while human capital had a negative impact. Placebo and parallel trend tests confirmed the robustness of these findings. Heterogeneity analysis showed that the positive effect on volatility was particularly pronounced in the Eastern region. Regarding high-quality economic development (HQD), the results (Table 5) indicated a significant positive impact of the carbon trading pilot policy. The effect was consistent across different regression models (OLS, random effects, fixed effects). Again, placebo and parallel trend tests confirmed robustness. Heterogeneity analysis revealed that the positive effect on HQD was statistically significant only in the Western region. Table 7 provides the high-quality development index for each province from 2005 to 2021.

The findings partially support the Porter Hypothesis, showing that while carbon trading pilot programs may initially increase economic volatility, they contribute positively to high-quality economic development in the long run. The short-term volatility might be attributed to the adjustment costs associated with emission reduction, increased carbon prices, and the still-developing nature of the carbon market. The regional heterogeneity observed reflects the varying levels of economic development, industrial structure, and institutional capacity across China. The positive impact on high-quality development in the West could be due to a lower initial level of industrialization and consequently lower adjustment costs associated with low-carbon transition. This contrasts with the East, where significant industrial restructuring and adjustment costs might contribute to short-term volatility.

This study contributes to the literature by providing robust evidence on the differentiated short-term and long-term effects of China's carbon trading pilot programs. The short-term increase in economic volatility needs to be managed through appropriate policy measures. The long-term benefits for high-quality economic development, particularly in less developed regions, should be leveraged to promote sustainable growth. Future research should delve deeper into the regional heterogeneity, exploring specific factors that mediate the effects of carbon trading policies on economic performance.

While the study employs rigorous econometric methods and robustness checks, several limitations exist. The analysis relies on observable data, potentially omitting unobservable factors that influence economic stability and development. The specific measurement of high-quality economic development may also influence the results. The analysis focuses on the impact of the pilot programs and cannot directly extrapolate to the impact of the national carbon market. Further research could incorporate more nuanced measures of both economic stability and high-quality development and explore the mediating roles of institutional factors.